Skip to main content
Entropy logoLink to Entropy
. 2021 Apr 11;23(4):450. doi: 10.3390/e23040450

A Survey of Function Analysis and Applied Dynamic Equations on Hybrid Time Scales

Chao Wang 1, Ravi P Agarwal 2,*
Editor: Jose A Tenreiro Machado
PMCID: PMC8069181  PMID: 33920402

Abstract

As an effective tool to unify discrete and continuous analysis, time scale calculus have been widely applied to study dynamic systems in both theoretical and practical aspects. In addition to such a classical role of unification, the dynamic equations on time scales have their own unique features which the difference and differential equations do not possess and these advantages have been highlighted in describing some complicated dynamical behavior in the hybrid time process. In this review article, we conduct a survey of abstract analysis and applied dynamic equations on hybrid time scales, some recent main results and the related developments on hybrid time scales will be reported and the future research related to this research field is discussed. The results presented in this article can be extended and generalized to study both pure mathematical analysis and real applications such as mathematical physics, biological dynamical models and neural networks, etc.

Keywords: dynamic equations, time scales, general theory

MSC: 34N05, 26E70

1. Basic Knowledge on Time Scales

In 1988, S. Hilger initiated the theory of time scales in his PhD thesis [1] to unify continuous and discrete analysis. The theory is more general and versatile than the traditional theories of differential and difference equations since it is an optimal way to accurately depict the continuous-discrete hybrid processes under one framework and have been widely applied to physics, chemical technology, population dynamics, biotechnology and economics, neural networks, and social sciences. It is well-known that the dynamic equations with time scale form contains, links, and extends the classical theory of differential and difference equations. Since a time scale is an arbitrary nonempty closed subset of R, we will have a result for difference equations if T=Z and obtain a result for differential equations if T=R. This theory represents a powerful tool for applications to economics, population models, and quantum physics, among others. Not only does the new theory of the so-called “dynamic equations” unify the theories of differential equations and difference equations, but it also extends these classical cases to cases “in between,” e.g., to the so-called q-difference equations when T=qN0¯:={qt:tN0forq>1}{0} or T=qZ¯:=qZ{0} (which has important applications in quantum theory) and can be applied on different types of time scales like T=hN,T=N2 and T=Tn the space of the harmonic numbers. Therefore, dealing with problems of differential equations on time scales becomes very important and meaningful in function analysis and applied dynamic equations.

In the sequel, we will provide some necessary knowledge that will be used in this review article.

A time scale T is a closed subset of R. It follows that the jump operators σ,ϱ:TT are defined by σ(t)=inf{sT:s>t} and ϱ(t)=sup{sT:s<t} with a stipulation that inf=supT (i.e., σ(t)=t if T has a maximum t) and sup=infT (i.e., ρ(t)=t if T has a minimum t), where denotes the empty set. If σ(t)>t, we say t is right scattered, while if ρ(t)<t we say t is left-scattered. Points that are right-scattered and left-scattered at the same time are called isolated. In addition, if t<supT and σ(t)=t, then t is called right-dense, and t>infT and ρ(t)=t, then t is called left-dense. Points that are right dense and left-dense at the same time are called dense. The mapping ν:T[0,) such that ν(t)=tρ(t) is called the backward graininess function, the mapping μ:T[0,) such that μ(t)=σ(t)t is called the forward graininess function. Note that both σ(t) and ρ(t) are in T when tT, this is because T is a closed subset of R. Define

Tκ=Tρ(sup(T)),supTTif supT<,Tif supT=.

Likewise, Tκ is defined as the set Tκ=TinfT,σ(infT)T if |infT|< and Tκ=T if infT=. If f:TR is a function, then the function fσ,fρ:TR is defined by fσ(t)=fσ(t) and fρ(t)=fρ(t) for all tT, respectively, i.e., fσ=fσ and fρ=fρ.

Throughout the paper, for the intervals on time scales, we make the assumption that a and b are the points in T. For ab, we will denote the time scale interval

[a,b]T={tT:atb}.

Open intervals and half-open intervals, etc. are defined accordingly. Note that [a,b]Tκ=[a,b]T if b is left-dense and [a,b]Tκ=[a,b)T=[a,ρ(b)]T if b is left-scattered. Similarly, ([a,b]T)κ=[a,b]T if a is right-dense and ([a,b]T)κ=(a,b]T=[σ(a),b]T if a is right-scattered.

1.1. Some Basic Knowledge of Δ-Calculus

Definition 1

([2,3]). A function f:TR is called regulated provided its right-sided limits exist (finite) at all right-dense points in T and its left-sided limits exist (finite) at all left-dense points in T.

Definition 2

([2,3]). The function f:TR is called rd-continuous provided that it is continuous at each right-dense point and has a left-sided limit at left dense points. The set of rd-continuous functions f:TR will be denoted in this book by Crd(T)=Crd(T,R). The set of functions f:TR that are Δ-differentiable and whose derivative is rd-continuous is denoted by Crd1(T)=Crd1(T,R).

Definition 3

([2,3]). Assume f:TR is a function and let tTκ. Then, we define fΔ(t) to be the number (provided it exists) with the property that given any ε>0, there exists a neighborhood U of t (i.e., U=(tδ,t+δ)T for some δ>0) such that

|f(σ(t))f(s)fΔ(t)[σ(t)s]|ε|σ(t)s|

for all sU, we call fΔ(t) the delta (or Hilger) derivative of f at t. A function F:TR is called an antiderivative of f:TR provided

FΔ(t)=f(t)holds for all tTκ,

and we define the Cauchy delta integral of f by

atf(s)Δs=F(t)F(a)for all t,aT.

Theorem 1

([2,3]). Assume f,g:TR are differentiable at tTκ. Then:

  • (i)
    The sum f+g:TR are differentiable at t with
    (f+g)Δ(t)=fΔ(t)+gΔ(t).
  • (ii)
    For any constant α,αf:TR is differentiable at t with
    (αf)Δ=αfΔ(t).
  • (iii)
    The product fg:TR is differentiable at t with
    (fg)Δ(t)=fΔ(t)g(t)+fσ(t)gΔ(t)=f(t)gΔ(t)+fΔ(t)gσ(t).
  • (iv)
    If f(t)fσ(t)0, then 1f is differentiable at t with
    1fΔ(t)=fΔ(t)f(t)fσ(t).
  • (v)
    If g(t)gσ(t)0, then fg is differentiable at t and
    fgΔ(t)=fΔ(t)g(t)f(t)gΔ(t)g(t)gσ(t).

Theorem 2

([2,3]). If a,b,cT,α,βR, and f,gCrd, then

  • (i)

    abαf(t)+βg(t)Δt=αabf(t)Δt+βabg(t)Δt;

  • (ii)

    abf(t)Δt=baf(t)Δt;

  • (iii)

    acf(t)Δt=abf(t)Δt+bcf(t)Δt;

  • (iv)

    |abf(t)Δt|ab|f(t)|Δt.

Definition 4

([2,3]). For h>0, we define the Hilger complex numbers, the Hilger real axis, the Hilger alternating axis, and the Hilger imaginary circle as

Ch:=zC:z1h,
Rh:=zCh:zRandz>1h,
Ah:=zCh:zRandz<1h,
Ih:=zCh:|z+1h|=1h,

respectively. For h=0, let C0:=C,R0:=R,I0=iR, and A0:=.

Definition 5

([2,3]). Let h>0 and zCh. We define the Hilger real part of z by

Reh(z):=|zh+1|1h

and the Hilger imaginary part of z by

Imh(z):=Arg(zh+1)h,

where Arg(z) denotes the principle argument of z (i.e., π<Arg(z)π). Note that Reh(z) and Imh(z) satisfy

1h<Reh(z)<andπh<Imh(z)πh,

respectively. In particular, Reh(z)Rh.

Definition 6

([2,3]). Let πh<ωπh. We define the Hilger purely imaginary number ι˚ω by

ι˚ω=eiωh1h.

For zCh, ι˚Imh(z)Ih.

Theorem 3

([2,3]). If the “circle plus” addition is defined by zω:=z+ω+zωh, then (Ch,) is an Abelian group. For zCh, we have z=Reh(z)ι˚Imh(z).

Definition 7

([2,3]). The “circle minus” substraction on Ch is defined by zω:=z(ω), where ω:=ω1+ωh.

For h>0, let Zh be the strip

Zh:=zC:πh<Im(z)πh,

and for h=0, let Z0:=C.

Definition 8

([2,3]). For h>0, the cylinder transformation ξh:ChZh by

ξh(z)=1hLog(1+zh),

where Log is the principal logarithm function. For h=0, we define ξ0(z)=z for all zC.

We define addition on Zh by

z+ω:=z+ωmod2πihfor z,ωZh. (1)

Theorem 4

([2,3]). The inverse transformation of the cylinder transformation ξh when h>0 is given by

ξh1(z)=1h(ezh1)

for zZh. For h=0, ξ01(z)=z.

Theorem 5

([2,3]). The cylinder transformation ξh is a group homomorphism from (Ch,) onto (Zh,+), where the addition + on Zh is defined by (1).

Definition 9

([2,3]). A function p:TR is called μ- regressive provided 1+μ(t)p(t)0 for all tTκ. The set of all regressive and rd-continuous functions p:TR will be denoted by R=R(T)=R(T,R). We define the set R+=R+(T,R)={pR:1+μ(t)p(t)>0,tT}. The set of all regressive functions on a time scale T forms an Abelian group under the addition defined by pq:=p+q+μpq.

Definition 10

([2,3]). If r is a μ-regressive function, then the generalized exponential function er is defined by

er(t,s)=expstξμ(τ)(r(τ))Δτ

for all s,tT, where the μ-cylinder transformation is as in

ξh(z):=1hLog(1+zh).

Theorem 6

([2,3]). Assume that p,q:TR are two μ-regressive functions. Then,

  • (i)

    e0(t,s)1 and ep(t,t)1;

  • (ii)

    ep(σ(t),s)=(1+μ(t)p(t))ep(t,s);

  • (iii)

    ep(t,s)=1ep(s,t)=ep(s,t);

  • (iv)

    ep(t,s)ep(s,r)=ep(t,r);

  • (v)

    (ep(t,s))Δ=(p)(t)ep(t,s).

1.2. Some Basic Knowledge of ∇-Calculus

In this subsection, we will introduce some basic knowledge of ∇-Calculus.

Definition 11

([2,3]). The function f:TR is called ld-continuous provided that it is continuous at each left-dense point and has a right-sided limit at right-dense points. The set of ld-continuous functions f:TR is denoted by Cld(T)=Cld(T,R). The set of functions f:TR that are -differentiable and whose derivative is ld-continuous is denoted by Cld1(T)=Cld1(T,R).

Definition 12

([2,3]). The function f:TR is called ld-continuous provided that it is continuous at each left-dense point and has a right-sided limit at each point, write fCld(T)=Cld(T,R). Let tTκ. Then, we define f(t) to be the number (provided it exists) with the property that given any ε>0, there exists a neighborhood U of t (i.e., U=(tδ,t+δ)T for some δ>0) such that

|f(ρ(t))f(s)f(t)[ρ(t)s]|ε|ρ(t)s|

for all sU, we call f(t) the nabla derivative of f at t. A function F:TR is called an antiderivative of f:TR provided

F(t)=f(t)holds for all tTκ,

and we define the Cauchy nabla integral of f by

atf(s)s=F(t)F(a)for all t,aT.

Definition 13

([2,3]). A function p:TR is called ν- regressive provided 1ν(t)p(t)0 for all tTk. The set of all regressive and ld-continuous functions p:TR will be denoted by Rν=Rν(T)=Rν(T,R). We define the set Rν+=Rν+(T,R)={pRν:1ν(t)p(t)>0,tT}. We define circle plus addition by pνq=p(t)+q(t)ν(t)p(t)q(t) for all tTκ.

Theorem 7

([2,3]). The set (Rν,ν) is an Abelian group.

Definition 14

([2,3]). For pRν, define circle minus by

νp=p1νp.

Definition 15

([2,3]). If r is a regressive function, then the generalized exponential function e^r is defined by

e^r(t,s)=expstξ^ν(τ)(r(τ))τ

for all s,tT, where the ν-cylinder transformation is as in

ξ^h(z):=1hLog(1zh).

Lemma 1

([2,3]). Assume that p,q:TR are two ν-regressive functions. Then,

  • (i)

    e^0(t,s)1 and e^p(t,t)1;

  • (ii)

    e^p(ϱ(t),s)=(1ν(t)p(t))e^p(t,s);

  • (iii)

    e^p(t,s)=1e^p(s,t)=eνp(s,t);

  • (iv)

    e^p(t,s)e^p(s,r)=e^p(t,r);

  • (v)

    (e^νp(t,s))=(νp)(t)e^νp(t,s).

2. Almost Periodic and Almost Automorphic Theory on Time Scales

Almost periodic phenomena are very common and almost periodic theory plays a significant role in natural science. Almost periodicity is an important feature of dynamical systems that will inaccurately retrace their paths through phase space, for example, for a planetary system, all the planets in orbits move in commensurable periods (i.e., a period vector is not proportional to a vector of integers). In mathematics, within any desired level of precision of periodicity, an almost periodic function is a real function with a suitably long, well-distributed “almost-periods”. The concept was first studied by H. Bohr and later generalized by V. Stepanov, H. Weyl and A.S. Besicovitch, and John von Neumann (see [4,5,6]), etc.

Compared with periodic phenomenon, almost periodic phenomenon can describe many regular changes in nature more accurately. Almost automorphic function, as a generalization of almost periodic function, has a wider range of applications. This notion was proposed by W.A. Veech (see [7,8]) and was found in the study of differential geometry related to physics, then more and more attention has been paid to the research on the generalization of corresponding concepts and their series (see [9,10]).

In this section, we will demonstrate some main results and recent developments of almost periodic and almost automorphic theory on translation time scales and extend the topic to more complicated hybrid time cases under the matched spaces of time scales.

2.1. Almost Periodic and Almost Automorphic Theory on Translation Time Scales

The theory of almost periodic and almost automorphic functions have wide applications in dynamic equations (see [9]). Through using the time scale theory initiated by Hilger (see [1]), many classical results of almost periodic and almost automorphic functions were extended to different time scales. The translation doublication of two time scales is the basic requirement of introducing the notions of almost periodic and almost automorphic functions. In 2016, Wang and Agarwal et al. (see [11,12,13]) proposed some equivalent concepts of periodic time scales as follows:

Definition 16

([12,13]). A time scale T is called a periodic time scale (or a translation invariant time scale) if Π:={τR:TTτ=T}{0},, where Tτ={t+τ:tT}.

We can obtain that, if we choose nonzero real number τΠ, then T=Tτ if and only if T is invariant under translations.

Definition 17

([12,13]). A time scale T is called a periodic time scale (or a translation invariant time scale) if Π:={τR:TτTτT}{0},.

Remark 1.

According to Definitions 16 and 17, the translation invariance of a time scale implies that the time scale T coincides with the obtained time scale Tτ through a translation number τR.

Example 1.

The following time scales are invariant:

  • (i)

    T=hZ, where h>0, has period P=h.

  • (ii)

    T={t=kqm:kZ,mN0}, where 0<q<1, has period P=1.

  • (iii)

    T=R has an arbitrary period PR{0}.

  • (iv)

    i=(2i1)h,2ih, h>0, has period P=2h.

Based on Definitions 16 and 17, some corrected concepts of almost periodic functions were proposed (see [11,14]). In [15], some sufficient conditions were obtained for the existence and exponential stability of piecewise mean-square almost periodic solutions of the impulsive stochastic Nicholson’s blowflies model on translation time scales. In [16,17,18,19,20], the authors firstly introduced the concept of piecewise almost periodic and almost automorphic functions on time scales with periodicity and applied them to analyze the almost periodic solutions to neural networks and biological dynamic models.

Definition 18

([16,18]). We say φ:TRn is piecewise rd-continuous with respect to a sequence {τi}T which satisfies τi<τi+1,iZ, if φ(t) is continuous on [τi,τi+1)T and rd-continuous on T{τi}. Furthermore, [τi,τi+1)T,iZ, are called intervals of continuity of the function φ(t).

Definition 19

([16,18]). For any ε>0, let ΓεΠ be a set of real numbers and {τi}T. We say {τij},i,jZ is equipotentially almost periodic on a periodic time scale T if for rΓεΠ, there exists at least one integer k such that

|τikr|<ε,for all iZ.

In the following, we will give the definition of piecewise rd-continuous almost periodic functions with respect to the sequence {τi,}iZ on a periodic time scale T.

Definition 20

([16,18]). Let T be a periodic time scale and assume that {τi}T satisfying the derived sequence {τij},i,jZ, is equipotentially almost periodic. A function φPCrd(T,Rn) is said to be piecewise rd-continuous almost periodic (short for rd-piecewise almost periodic) if:

  • (i)

    for any ε>0, there is a positive number δ=δ(ε) such that if the points t and t belong to the same interval of continuity and |tt|<δ, then φ(t)φ(t)<ε;

  • (ii)

    for any ε>0, there is relative dense set ΓεΠ of ε-almost periods such that if τΓε, then φ(t+τ)φ(t)<ε for all tT, which satisfies the condition |tτi|>ε,iZ.

Based on Definitions 18–20, some basic properties of piecewise almost periodic functions were obtained.

Theorem 8

([16,18]). If φPCrd(T,Rn) is rd-piecewise almost periodic, then, for any ε>0, there exists a relative dense set of intervals of a fixed length γεΠ, which consist of ε-almost periods of the function φ(t).

Theorem 9

([16,18]). Let φPCrd(T,Rn) be an rd-piecewise almost periodic function with values in the set ERn. If F(y) is an uniformly continuous function defined on the set E, then the function Fφ(t) is rd-piecewise almost periodic in t.

Theorem 10

([16,18]). For any two rd-piecewise almost periodic functions with respect to the same sequence {τi}T, for any ε>0, there exists a relative dense set of their common ε-almost periods.

In fact, the above Definitions 18–20 can be generalized to Banach spaces and some basic theorems can be established in Banach space.

Now, introduce the set

B={tk}:tkT,tk<tk+1,kZ,limk±tk=±,

which denotes all unbounded increasing sequences of real numbers.

Let X be a Banach space, Ω be an open set in X or Ω=X, and S denotes an arbitrary compact subset of Ω.

Definition 21

([16,18]). The functions f,gPCrd(T×Ω,X) are said to be ε-equivalent uniformly for xΩ or f,g possess uniform ε-equivalence for xΩ, and denote fεg, if for all ε>0 and for each compact subset S of Ω, the following conditions hold:

  • (i)

    The points of possible discontinuity of these functions can be enumerated tkf,tkg, admitting a finite multiplicity by the order in T, so that |tkftkg|<ε.

  • (ii)
    There exist strictly increasing sequences of numbers {tk},{tk}, tk<tk+1,tk<tk+1,kZ, for which we have
    supt(tk,tk+1)T,t(tk,tk+1)Tf(t,x)g(t,x)<ε,|tktk|<ε,xS,kZ.

Theorem 11

([16,18]). Let φPCrd(T×Ω,X) be rd-piecewise almost periodic in t uniformly for xΩ. Then, it is uniformly rd-continuous on TB and bounded on T×S.

Let T,PB and let s(TP):BB be a map such that the set s(TP) forms a strictly increasing sequence. For DR and 0<hΠ, we introduce the notations θh(D)={t+h:tD},Fh(D)=D{θh(D)}. Denote by ϕ=φ(t),T the element from the space PCrd(T×Ω,X)×B and, for every sequence of real numbers {sn},n=1,2, with θsnϕ=φ(t+sn,x),T+sn, we shall consider the sets φ(t+sn,x),T+snPCrd×B, where

T+sn:=Tsn={tk+sn:kZ,n=1,2,}.

For convenience, we introduce the translation operator S, and let us denote by Sα+βϕ and SαSβϕ the limits limnθαn+βn(ϕ) and limnθαn(limmθβmϕ), respectively, and are written only when the limits exist.

Theorem 12

([16,18]). The function φPCrd(T×Ω,X) is rd-piecewise almost periodic in t uniformly for xΩ with respect to a sequence TB if and only if from every pair of sequence α,β, one can extract common subsequences αα,ββ such that

Sα+βϕ=SαSβϕ

exists pointwise, where ϕ=φ(t,x),T.

We established the following piecewise almost periodic solution of the dynamic equations on hybrid time scales.

First, we shall consider the linear dynamic equations as follows:

xΔ=A(t)x,ttk,Δ˜x(tk)=Bkx(tk),t=tk,kZ, (2)

where tT,{tk}B,APCrd(T,Rn×n),BkRn×n,kZ.

By x(t)=x(t;t0,x0), we denote the solution of (2) with initial condition by x(t0+)=x0,x0Rn. Assume the following conditions hold:

  • (H1)

    The matrix-valued function APCrd(T,Rn×n) is almost periodic.

  • (H2)

    {Bk},kZ is an almost periodic sequence.

  • (H3)

    det(E+Bk)0,kZ, where E is the identity matrix.

  • (H4)

    The set of sequence {tkj},tkj=tk+jtk,kZ,jZ is equipotentially almost periodic and infktk1=θ>0.

Now, consider the following system:

xΔ=A(t)x+f(t),ttk,Δ˜x(tk)=Bkx(tk)+Ik,t=tk,kZ. (3)

Theorem 13

([16,18]). If (H1)(H4) hold, (2) admits an exponential dichotomy on T with a projection P, then (3) admits a piecewise rd-continuous almost periodic solution as follows:

x(t)=tX(t)PX1(σ(s))f(s)Δst+X(t)(EP)X1(σ(s))f(s)Δs+<tk<tX(t)PX1(tk)Ikt<tk<+X(t)(EP)X1(tk)Ik,

where X(t) is a fundamental matrix solution of system (2).

In the following part, based on the translation hybrid time scales, the definition of ld-piecewise continuous functions on time scales was introduced and some basic properties of piecewise ld-continuous weighted pseudo almost automorphic functions were established.

Definition 22

([20]). We say φ:TX is piecewise ld-continuous with respect to a sequence {tk}T which satisfies tk<tk+1,kZ, if φ(t) is continuous on (tk,tk+1]T and ld-continuous on T{tk}. Furthermore, (tk,tk+1]T are called intervals of continuity of the function φ(t).

For simplicity, let PCld(T,X) be the set of all piecewise ld-continuous functions with respect to a sequence {tk},kZ and X be a Banach space. For {tk}kZB, the notation BPCld(T,X) denotes the space constituted by all bounded piecewise ld-continuous functions ϕ:TX with the property that ϕ(·) is ld-continuous at t for any t{tk}kZ and ϕ(tk)=ϕ(tk) for all kZ. The symbol Ω denotes a subset of X and BPCld(T×Ω,X) denotes the space constituted by by all bounded piecewise functions which are ld-continuous in t, ϕ:T×ΩX with the property that, for any xΩ,ϕ(·,x)BPCld(T×X,X). Moreover, ϕ(t,·) is continuous at xΩ for any tT.

Now, we use the symbol UPCld(T,X) to denote the space of all functions φPCld(T,X) with the property that for any ε>0, there exists a positive number δ=δ(ε) such that if the left-dense points t,t belong to the same interval of continuity of φ and |tt|<δ, then φ(t)φ(t)<ε.

Furthermore, T,PB and s(TP):BB is a map with the property that the set s(TP) constitutes a strictly increasing sequence. For DR and ε>0, the notations θε(D)={t+ε:tD},Fε(D)=D{θε(D)}. We use the symbol ϕ˜=(φ(t),T) to denote the element from the space PCld(T,X)×B. For every sequence of real numbers {sn},n=1,2, with θsnϕ˜:=(φ(t+sn),Tsn), the sets {φ(t+sn),Tsn}PCld×B will be considered, where

Tsn={tksn:kZ,n=1,2,}.

Definition 23

([20]). Let {tk}B,kZ. We say {tkj} is a derivative sequence of {tk} and

tkj=tk+jtk,k,jZ.

Definition 24

([20]). Let tkj=tk+jtk,k,jZ. We say {tkj},k,jZ is equipotentially almost automorphic on a periodic time scale T if for any sequence {sn}Z, there exists a subsequence {sn} such that

limntksn=γk

is well defined for each kZ and

limnγksn=tk

for each kZ.

Definition 25

([20]). A function ϕPCld(T,X) is said to be piecewise ld-continuous almost automorphic (short for ld-piecewise almost automorphic) if the following conditions are fulfilled:

  • (i)

    Let T={tk} be an equipotentially almost automorphic sequence.

  • (ii)
    Let φPCld(T,X) be a bounded function with respect to a sequence T={tk}. We say that φ is ld-piecewise almost automorphic if, from every sequence {sn}n=1Π, we can extract a subsequence {τn}n=1 such that
    ϕ˜=φ(t),T=limnφ(t+τn),Tτn=limnθτnϕ˜
    is well defined for each tT and
    ϕ˜=φ(t),T=limnφ(tτn),T+τn=limnθτnϕ˜
    for each tT. Denote by AApl(T,X) the set of all such functions.
  • (iii)

    A bounded function fPCld(T×X,X) with respect to a sequence T={tk} is said to be ld-piecewise uniformly almost automorphic if f(t,x) is ld-piecewise automorphic in tT uniformly in xB, where B is any bounded subset of X. Denote by AApl(T×X,X) the set of all such functions.

Similarly, we can also introduce the concept of piecewise almost automorphic functions which belong to PCrd(T,X).

Some basic properties of piecewise almost automorphic functions were obtained as follows.

Let U be the set of all functions ρ^:T(0,) which are positive and locally ∇-integrable over T. For a given r[0,)Π and t0T, set

m(r,ρ^,t0):=t0rt0+rρ^(s)s (4)

for each ρ^U.

Remark 2.

In (4), if T=R, t0=0, one can easily get

m(r,ρ^,t0):=rrρ^(s)ds

if T=Z, t0=0, one has the following:

m(r,ρ^,t0)=k=r+1rρ^(k).

Define

U:=ρ^U:limrm(r,ρ^,t0)=,
UB:=ρ^U:ρ^is bounded andinfsTρ^(s)>0.

It is clear that UBUU. Now, for ρ^U, define

PAA0pl(T,ρ^):={ϕBPCld(T,X):limr1m(r,ρ^,t0)t0rt0+rϕ(s)ρ^(s)s=0,t0T,rΠ}.

Similarly, we define

PAA0pl(T×X,ρ^):={ΦBPCld(T×Ω,X):limr1m(r,ρ^,t0)t0rt0+rΦ(s,x)ρ^(s)s=0uniformly with respect toxK,t0T,rΠ}.

We are now ready to introduce the sets WPAApl(T,ρ^) and WPAApl(T×X,ρ^) of piecewise ld-continuous weighted pseudo almost automorphic functions:

WPAApl(T,ρ^)=f=g+ϕPCld(T,X):gAApl(T,X)andϕPAA0pl(T,ρ^),
WPAApl(T×X,ρ^)={f=g+ϕPCld(T×X,X):gAApl(T×X,X)andϕPAA0pl(T×X,ρ^)}.

Theorem 14

([20]). Let f=g+ϕWPAApl(T×X,ρ^), where gAApl(T×X,X), ϕPAA0pl(T×X,ρ^),ρ^UB and the following conditions hold:

  • (i)

    f(t,x):tT,xK is bounded for every bounded subset KΩ.

  • (ii)

    f(t,·),g(t,·) are uniformly continuous in each bounded subset of Ω for all tT.

Then, f·,h(·)WPAApl(T,ρ^) if hWPAApl(T,ρ^) and h(T)Ω.

Theorem 15

([20]). A necessary and sufficient condition for a bounded sequence {an} to be in WPAApl(Z,ρ^) is that there exists a uniformly ld-continuous function fWPAApl(T,ρ^) and a discretization partition {tn} such that f(tn)=an,nZ,ρ^UB.

Theorem 16

([20]). Assume that ρ^UB and the sequence of vector-valued functions {Ii}iZ is weighted pseudo almost automorphic, i.e., for any xΩ,{Ii(x),iZ} is weighted pseudo almost automorphic sequence. Suppose {Ii(x):iZ,xK} is bounded for every bounded subset KΩ, Ii(x) is uniformly continuous in xΩ uniformly in iZ. If hWPAApl(T,ρ^)UPCld(T,X) such that h(T)Ω, then Iih(ti) is a weighted pseudo almost automorphic sequence.

Through using the above basic theorems, one can study the almost automorphic solutions of the following dynamic equations on time scales.

Abstract impulsive ∇-dynamic equations as follows:

x(t)=A(t)xϱ+ft,x(t),tT,tti,iZ,Δx(ti)=x(ti+)x(ti)=Iix(ti),t=ti, (5)

where APCld(T,X) is a linear operator in the Banach space X and fPCld(T×X,X),xϱ=x(ϱ(t)). Now, f,Ii,ti satisfy suitable conditions that will be given later and T is an almost periodic time scale. In addition, the notations x(ti+) and x(ti) represent the right-hand and the left-hand side limits of x(·) at ti, respectively.

In the following, consider the abstract dynamic system (5) with the following assumptions:

  • (H1)
    The family {A(t):tT} of operators in X generates an exponentially stable evolution system {T(t,s):ts}, i.e., there exist K0>1 and ω>0 such that
    T(t,s)K0e^νω(t,s),ts,
    and for any sequence {sn}Π, there exists a subsequence {sn}{sn} such that
    limnT(t+sn,s+sn)=T(t,s)is well defined for each t,sT,ts.
  • (H2)

    f=g+ϕWPAA(T,ρ^), where ρ^U and f(t,·) is uniformly continuous in each bounded subset of Ω uniformly in tT; Ii is a weighted pseudo almost periodic sequence, Ii(x) is uniformly continuous in xΩ uniformly in iZ, infiZti1=θ>0.

Theorem 17

([20]). Let f·,ϑ(·)WPAA(T,ρ^), where ϑWPAA(T,ρ^) and {T(t,s),ts} is exponentially stable, ρ^U. Then,

F(·):=(·)T(·,s)fs,ϑ(s)s+ti<·T(·,ti)Iiϑ(ti)WPAA(T,ρ^).

According to Theorem 17, the following existence result of almost automorphic solutions was obtained.

Theorem 18

([20]). Assume the following conditions hold:

  • (A1)
    The family {A(t):tT} of operators in X generates an exponentially stable evolution system {T(t,s):ts}, i.e., there exist K0>1 and ω>0 such that
    T(t,s)K0e^νω(t,s),ts,
    and, for any sequence {sn}Π, there exists a subsequence {sn}{sn} such that
    limnT(t+sn,s+sn)=T(t,s)is well defined for each t,sT,ts.
  • (A2)
    fWPAA(T×Ω,ρ^), and f satisfies the Lipschitz condition with respect to the second argument, i.e.,
    f(t,x)f(t,y)L1xy,tT,x,yΩ,
  • (A3)
    Ii is a weighted pseudo almost periodic sequence, and there exists a number L2>0 such that
    Ii(x)Ii(y)L2xy,
    for all x,yΩ,iZ.

Suppose that

K0L1(1ν_ω)ω+K0L21e^νω(θ,0)<1.

Then, (5) has a unique weighted piecewise pseudo almost automorphic mild solution, where e^νω(θ,0):=supiZe^νω(ti+1,ti).

In [21,22], the Π-semigroup and the semigroups induced by complete-closed time scales were introduced to study the almost periodic mild solutions to evolution equations.

Let Π+=[0,+)Π and X be a Banach space, and Tτ:XX be a transformation. Obviously, {Tτ:τΠ} is a set containing only one parameter. We introduce the multiplication as follows:

Tτ1Tτ2=Tτ1+τ2. (6)

It follows that

Tτ1Tτ2Tτ3=Tτ1Tτ2Tτ3=Tτ1+τ2+τ3,

I=T0 is the identity, and Tτ is the inverse element of Tτ.

Theorem 19

([21]). {Tτ:τΠ} is an operator group with respect to the multiplication defined by (6). It is an Abelian group.

According to Theorem 19, some basic concepts which will be needed to define a Π-semigroup for an invariant time scale under translations can be introduced as follows.

Definition 26

([21]). Let a time scale T be invariant under translations, and {Tτ} be a family of bounded linear operators on Banach space X. If, for all τ1,τ2Π+, the following holds:

Tτ1+τ2=Tτ1Tτ2, (7)

then {Tτ:τΠ+} is called a one-parameter operator semigroup; if (7) holds for all τΠ, we call {Tτ:τΠ} a one-parameter operator group.

Definition 27

([21]). Let T be an invariant time scale under translations, and {Tτ:τΠ+} be an operator group on a Banach space X, i.e.,

Tτ1Tτ2=Tτ1+τ2,τ1,τ2Π+,T0=I.

If, for every τ00 and any ε>0, there is a neighborhood U of τ0(i.e., U=(τ0δ,τ0+δ)Π+ for some δ>0) such that

TτxTτ0x<εfor all τU,

then we call {Tτ:τΠ+} the strong-continuous operator semigroup or the Π-semigroup.

Theorem 20

([21]). Let a time scale T be invariant under translations, and {Tτ:τΠ+} be an operator semigroup on the Banach space X. For any ε>0 and xX, there exists a neighborhood U=(τ1δ,τ1+δ)Π+ for some δ>0, such that

T|σΠ(τ1)τ2|xxεfor all τ2U, (8)

then {Tτ:τΠ+} is a Π-semigroup.

In the following, the definition of infinitesimal generator of a Π-semigroup was introduced.

Definition 28

([21]). Let T be an invariant time scale under translations and {Tτ:τΠ+} be a Π-semigroup on a Banach space X. Let D denote a subset of X, which has the property that, for each xD, there exists a yX such that for any ε>0, there is a neighborhood U=(τ1δ,τ1+δ)Π+ for some δ>0 such that

(T|σΠ(τ1)τ2|I)xy|σΠ(τ1)τ2|<ε|σΠ(τ1)τ2|,τ2U. (9)

We define A:DX satisfying Ax=y, where y is fixed by (9). In what follows, we call this A the infinitesimal generator of this Π-semigroup.

Theorem 21

([21]). Let T be an invariant under translations time scale, {Tτ:τΠ+} be a Π-semigroup on Banach space X satisfying (8), and A be the infinitesimal generator of the Π-semigroup. Then, A is a closed densely defined operator and for every xD(A), the following holds:

(Tτx)ΔΠ=A(Tτx)=TτAx,

that is

(Tτx)x=0τATsxΔΠs=0τTsAxΔΠs,

where D(A) denotes the domain of the operator A and ΔΠ is the differential operator over the time scale Π.

Theorem 22

([21]). Let T be an invariant time scale under translations and X be a Banach space. Assume that {Tτ:τΠ+} is a Π-semigroup, A is the infinitesimal generator of the Π-semigroup and D(A)=X,eA(τ1+τ2,0)=eA(τ1,0)eA(τ2,0) for all τ1,τ2Π+. Then,

Tτ=eA(τ,0),τΠ+,

where D(A) denotes the domain of A.

Now, we introduce a new notion called the moving-operator on time scales.

Definition 29

([21]). Let A be the infinitesimal generator of the Π-semigroup. We call e˜A(t,t0),t0T the exponential function generated by A on the time scale T. We also let Tt=e˜A(t,t0) and call Tt the moving-operator on T.

Let X be a Banach space, and consider the following system:

xΔ=Ax(t),x(t0)=x0,t0T, (10)

where A is the infinitesimal generator of a Π-semigroup satisfying all the conditions in Theorem 22, and x:TX.

Theorem 23

([21]). The fundamental solution of the system (10) can be expressed as

x(t)=Ttx0,

From Theorem 23, the following result follows immediately.

Theorem 24

([21]). Let A be the infinitesimal generator of the Π-semigroup, and let Tt be the moving-operator on T. Then,

(Ttx)Δ=A(Ttx)=TtAx,

that is

(Ttx)x=t0tATsxΔs=t0tTsAxΔs.

In the following part, we will introduce two equivalent definitions of relatively dense sets on semigroups induced by complete-closed time scales under translations.

Definition 30

([22]). Let T be a complete-closed time scale. If

Π+:=[0,+)Π,{0},

then we say (Π+,+) is a positive direction semigroup induced by the time scale T; if

Π:=(,0]Π,{0},

then we say (Π,+) is a negative direction semigroup induced by the time scale T.

Now, we denote the set {1,2,,m} by Λ and introduce the following concept.

Definition 31

([22]). A subset E of a semigroup Π+ induced by time scales is relatively dense if there exists elements s1,s2,,sm in Π+ such that iΛ(si+E)=Π+, where si+E={si+e:eE}.

Definition 32

([22]). A subset E of Π+ is called relatively dense if there exists a positive number LΠ+ such that [a,a+L]Π+E for all aΠ+. The number L is called the inclusion length.

Theorem 25

([22]). Definition 31 is equivalent to Definition 32.

By Theorem 25, it is obvious that, for the Abelian group (Π,+), the following two definitions are also equivalent.

Definition 33

([22]). A subset E of a group Π induced by time scales is relatively dense if there exists elements s1,s2,,sm in Π such that iΛ(si+E)=Π, where si+E={si+e:eE}.

Definition 34

([22]). A subset E of Π is called relatively dense if there exists a positive number LΠ+ such that [a,a+L]ΠE for all aΠ. The number L is called the inclusion length.

Next, in [22], the equivalence of Bochner and Bohr almost automorphy on semigroup related to time scales was proved which play a fundamental role in studying the almost automorphic solutions for dynamic equations by using both notions.

Definition 35

([22]). Let T be a positive direction complete-closed time scale and (Π+,+) be a semigroup. A function f:TX is said to be almost automorphic function on the semigroup (Π+,+) if for any sequence α={αn}nNΠ+ of semigroup elements, there is a subsequence α={αn}nN and a sequence {α˜n}Π+ depending on α such that for each tT the equality

limnlimmf(t+αm+α˜n)=Tα˜Tαf=f(t)

holds on (Π+,+).

Definition 36

([22]). A bounded function f on a semigroup Π+ is said to be positive direction Bohr almost automorphic if, for each finite set NTT and prescribed ε>0, there is a set Bε=Bε(NT)Π+ such that

  • (i)

    Bε is relatively dense.

  • (ii)

    If τBε, then maxtNT|f(t+τ)f(t)|<ε.

  • (iii)

    If τ1,τ2Bε, then maxtNT|f(t+τ1+τ2)f(t)|<2ε.

Theorem 26

([22]). A function f on semigroup Π+ is a positive direction Bochner almost automorphic function if and only if it is a positive direction Bohr almost automorphic function.

Particularly, since the irregularity of time scales, the delay classification was addressed to solve the delay dynamic equations on hybrid time scales (see [23]).

The irregularity and the translation of time scales led to the idea of the approximation of time scales. In 2014, Wang and Agarwal (see [24]) firstly proposed the concept of almost periodic time scales with the approximation property as follows:

Definition 37

([11,12,13]). We say T is an almost periodic time scale, if for any given ε>0, there exists a constant l(ε)>0 such that each interval of length l(ε) contains a τ(ε)R such that d(T,Tτ)<ε, i.e., for any ε>0, the following set

E{T,ε}={τR:d(Tτ,T)ε}

is relatively dense in Π1. Here, τ is called the ε-translation number of T and l(ε) is called the inclusion length of E{T,ε}, E{T,ε} is called the ε-translation numbers set of T, and for simplicity, we use the notation E{T,ε}:=Πε and Π1:={τR:TTτ}{0}, where Tτ:=T+τ={t+τ:tT}.

Definition 37 was applied to study the almost periodicity and almost automorphy of time scales through translations and the notions of almost periodic and almost automorphic time scales were introduced (see [25]). Based on the results of approximation property of time scales, a new type of almost periodic functions called double-almost periodic functions was proposed and applied to study neural networks and biological dynamic models, and some new results of the existence and stability of the double-almost periodic solutions were established (see [26,27]). Moreover, these results were also extended to discontinuous cases and some notions of piecewise double-almost periodic functions and their generalizations were put forward and applied to study the impulsive dynamic equations and models (see [28,29,30,31]).

In 2015, to obtain the general results on more complicated hybrid time scales, the notion of changing-periodic time scales was introduced as follows:

Definition 38

([32,33]). Let T be an infinite time scale. We say T is a changing-periodic or a piecewise-periodic time scale if the following conditions are fulfilled:

  • (a)

    T=i=1TiTr and {Ti}iZ+ is a well connected timescale sequence, where Tr=i=1k[αi,βi] and k is some finite number, and [αi,βi] are closed intervals for i=1,2,,k or Tr=;

  • (b)

    Si is a nonempty subsets of R with 0Si for each iZ+ and Λ=i=1SiR0, where R0={0} or R0=;

  • (c)

    for all tTi and all ωSi, we have t+ωTi, i.e., Ti is an ω-periodic time scale;

  • (d)

    for ij, for all tTi{tijk} and all ωSj, we have t+ωT, where {tijk} is the connected points set of the timescale sequence {Ti}iZ+;

  • (e)

    R0={0} if and only if Tr is a zero-periodic time scale and R0= if and only if Tr=;

and the set Λ is called a changing-periods set of T, Ti is called the periodic sub-timescale of T and Si is called the periods subset of T or the periods set of Ti, Tr is called the remain time scale of T and R0 the remain periods set of T.

Definition 38 shows that one can discuss the almost periodic and almost automorphic approximation problems on any arbitrary time scales with a bounded graininess function μ. The following theorems play a fundamental role in establishing the basic theory of local almost periodic and almost automorphic functions and the related dynamic equations on time scales. Based on the following theorems, it is meaningful to conduct the related qualitative analysis of local almost periodic and almost automorphic dynamical behavior described by dynamic systems on arbitrary time scales in the future.

Theorem 27

([32,33], Decomposition Theorem of Time Scales). Let T be an infinite time scale and the graininess function μ:TR+ be bounded. Then, T is a changing-periodic time scale, i.e., there exists a countable periodic decomposition such that T=i=1TiTr and Ti is ω-periodic sub-timescale, ωSi,iZ+, where Ti,Si,Tr satisfy the conditions in Definition 38.

Theorem 28

([32,33], Periodic Coverage Theorem of Time Scales). Let T be an infinite time scale and the graininess function μ:TR+ be bounded. Then, T can be covered by countable periodic time scales.

On changing-periodic time scales, the local-periodic solutions for functional dynamic equations with infinite delay and the local pseudo almost automorphic solutions to semilinear dynamic equations were respectively discussed (see [34,35]).

Consider the following dynamic equation:

xΔ(t)=Ax(t)+ft,x(t),tT, (11)

where A is the infinitesimal generator of a Π-semigroup for the periodic sub-timescale Tτt, x:TτtX,f:Tτt×XX.

Definition 39

([35]). A local mild solution to (11) is a continuous function x(t):TτtX satisfying

x(t)=Tt,t0τx(t0)+t0tTt,sτfs,x(s)Δτss

for all tt0 and all t0Tτt, where Tt,t0τ is the moving-operator on Tτt.

In [35], the following sufficient condition of the existence and uniqueness of the local pseudo almost automorphic mild solution to (11) was established under the following assumptions:

  • (H1)
    Let A be the infinitesimal generator of a Π-semigroup {Tτ:τSτt}. The moving-operator family Tt,t0τ:t,t0Tτt,tt0 is exponentially stable, that is, there exist K>0, ω>0 such that
    Tt,t0τKeωτ(t,t0),for all tTτt.
  • (H2)

    f:R×XX is local pseudo almost automorphic.

  • (H3)
    There exists a nonnegative function ϱ0(t)Lp(Tτt,R+)(p=1,2) such that
    f(t,x)f(t,y)ϱ0(t)xy
    for all x,yX and tTτt.

Theorem 29

([35]). Under assumption (H1)(H3), if Sτt{0} or Sτt+{0},, then (11) has a unique local pseudo almost automorphic mild solution.

2.2. Almost Periodic and Almost Automorphic Theory under Matched Spaces of Time Scales

In 2017, the notion of matched spaces of time scales was introduced by Wang and Agarwal et al. in [36,37,38]. Before giving the concept of matched spaces of time scales, we need the following definition.

Definition 40

([36,38]). Let the pair (Π,δ˜) be an Abelian group and Π,T be the largest open subsets of the time scales Π and T, respectively. Furthermore, let Π be the adjoint set of T and F the adjoint mapping between T and Π. The operator δ:Π×TT satisfies the following properties:

  • (P1)
    (Monotonicity) The function δ is strictly increasing with respect to its all arguments, i.e., if
    (T0,t),(T0,u)Dδ:=(s,t)Π×T:δ(s,t)T,
    then t<u implies δ(T0,t)<δ(T0,u); if (T1,u),(T2,u)Dδ with T1<T2, then δ(T1,u)<δ(T2,u).
  • (P2)

    (Existence of inverse elements) The operator δ has the inverse operator δ1:Π×TT and δ1(τ,t)=δ(τ1,t), where τ1Π is the inverse element of τ.

  • (P3)

    (Existence of identity element) There exists eΠΠ such that δ(eΠ,t)=t for any tT, where eΠ is the identity element in Π.

  • (P4)

    (Bridge condition) For any τ1,τ2Π and tT, δδ˜(τ1,τ2),t=δτ1,δ(τ2,t)=δτ2,δ(τ1,t).

Then, the operator δ(s,t) associated with eΠΠ is said to be a shift operator on the set T. The variable sΠ in δ is called the shift size. The value δ(s,t) in T indicates s units shift of the term tT. The set Dδ is the domain of the shift operator δ.

Then, the matched spaces of time scales can be defined as follows.

Definition 41

([36,38]). Let the pair (Π,δ˜) be an Abelian group, and Π,T be the largest open subsets of the time scales Π and T, respectively. Furthermore, let Π be an adjoint set of T and F the adjoint mapping between T and Π. If there exists the shift operator δ satisfying Definition 40, then we say the group (T,Π,F,δ) is a matched space for the time scale T.

By using Definition 41, the classical definitions of almost periodic functions and almost automorphic functions can be generalized as follows.

Definition 42

([39]). Let T be a periodic time scale under the matched space (T,Π,F,δ). A function fC(T×D,X) is called δ-almost periodic function with shift operators in tT uniformly for xD if the ε-shift set of f

E{ε,f,S}={τΠ˜:fδτ±1(t),xf(t,x)<ε,for all tT and xS}

is a relatively dense set with respect to the pair (Π,δ˜) for all ε>0 and for each compact subset S of D; that is, for any given ε>0 and each compact subset S of D, there exists a constant l(ε,S)>0 such that each interval of length l(ε,S) contains a τ(ε,S)E{ε,f,S} such that

fδτ±(t),xf(t,x)<ε,for all tT and xS.

Now, τ is called the ε-shift number of f and l(ε,S) is called the inclusion length of E{ε,f,S}.

Definition 43

([40]). (i) Let f:TX be a bounded continuous function. f is said to be δ-almost automorphic under the matched space (T,F,Π,δ) if for every sequence of real numbers {sn}n=1Π˜, one can extract a subsequence {τn}n=1Π˜ such that:

g(t)=limnfδτn(t)

is well defined for each tT and

limngδτn1(t)=limngδτn1(t)=f(t)

for each tT. Denote by AAδ(T,X) the set of all such functions.

  • (ii)

    A continuous function f:T×XX is said to be δ-almost automorphic if f(t,x) is δ-almost automorphic in tT uniformly for all xB, where B is any bounded subset of X. Denote by AAδ(T×X,X) the set of all such functions.

Definitions 42 and 43 are the basic concepts of almost periodic functions and almost automorphic functions on irregular time scales such as qZ¯,N±12, etc., and their basic properties were obtained as follows.

Theorem 30

([36,38,39]). Assume that fC(T×D,En) is δ-almost periodic in t uniformly for xD under the matched space (T,F,Π,δ), and δτ(t) is continuous in t. Then, it is uniformly continuous and bounded on T×S.

We introduce the moving-operator Tδ, Tαδf(t,x)=g(t,x) by

g(t,x)=limn+fδαn(t),x

and is written only when the limit exists. The mode of convergence, e.g., pointwise, uniform, etc., will be specified at each use of the symbol.

In the following, we will establish a shift-convergence theorem of δ-almost periodic functions.

Theorem 31

([36,38,39]). Assume that fC(T×D,En) is δ-almost periodic in t uniformly for xD under the matched space (T,F,Π,δ). Then, for any given sequence αΠ˜, there is a subsequence βα and gC(T×D,En) such that Tβδf(t,x)=g(t,x) holds uniformly on T×S. Furthermore, g(t,x) is δ-almost periodic in t uniformly for xD under the matched space (T,F,Π,δ).

Theorem 32

([36,38,39]). Assume that f(t,x)C(T×D,En) is δ-almost periodic in t uniformly for xD and φ(t) is δ-almost periodic with {φ(t):tT}S, then ft,φ(t) is δ-almost periodic.

Definition 44

([36,38,39]). Let f(t,x)C(T×D,En). Then, Hδ(f)={g(t,x):TEn| there is αΠ˜ such that Tαδf(t,x)=g(t,x) exists uniformly on T×S} is said to be the δ-hull of f(t,x) under the matched space (T,F,Π,δ).

Theorem 33

([36,38,39]). Hδ(f) is compact if and only if f(t,x) is δ-almost periodic in t uniformly for xD.

Theorem 34

([36,38,39]). If f(t,x) is δ-almost periodic in t uniformly for xD under the matched space (T,F,Π,δ), then for any g(t,x)Hδ(f) and Hδ(f)=Hδ(g).

Based on the theorems above, a sufficient and necessary criterion for δ-almost periodic functions was established.

Theorem 35

([36,38,39]). A function f(t,x) is δ-almost periodic in t uniformly for xD under the matched space (T,F,Π,δ) if and only if for every pair of sequences α,βΠ˜, there exist common subsequences αα,ββ such that

Tδ˜(α,β)δf(t,x)=TαδTβδf(t,x). (12)

In what follows, some basic properties of δ-almost automorphic functions were also established.

Next, the notation X denotes a Banach space endowed with the norm · and B(X,Y) the Banach space of all bounded linear operators from X to Y. This is simply denoted as B(X) when X=Y. Let BC(T,X) be the space of bounded continuous function from T to X with the supremum norm u=suptTu(t).

Theorem 36

([40,41]). AAδ(T,X) equipped with the norm · is a Banach space.

Theorem 37

([40,41]). Let (T,F,Π,δ) be a regular matched space. If g(t,x)AAδ(T×X,X) and α(t)AAδ(T,X), then G(t):=gt,α(t)AAδ(T,X).

Moreover, if the following assumptions hold:

  • (H1)

    f(t,x) is uniformly continuous in any bounded subset KX for all tT.

  • (H2)

    g(t,x) is uniformly continuous in any bounded subset KX for all tT.

Then, we can obtain the following theorem.

Theorem 38

([40,41]). Let f=g+ϕWPAAδ(T×X,ρ) where gAAδ(T×X,X), ϕPAA0δ(T×X,ρ),ρU. Assume that (H1) and (H2) are satisfied. Then, the L(·):=f·,h(·)WPAAδ(T,ρ) if hWPAAδ(T,ρ).

From Theorem 38, we can establish the following consequence:

Corollary 1

([40,41]). Let f=g+ϕWPAAδ(T,ρ) where ρU and assume both f and g are Lipschitzian in xX uniformly in tT. Then L(·):=f·,h(·)WPAAδ(T,ρ) if hWPAAδ(T,ρ).

It is very important to establish the approximation theory on non-translational shift time scales since that they may combine into more complicated hybrid time scales. In [38,41], the concept of the n0-order Δ-almost periodic functions and weighted pseudo δ-almost automorphic functions were introduced and studied, respectively, and their obtained basic properties were applied to the qualitative analysis of the related dynamic equations on hybrid domains.

Definition 45

([38]). Let T be a periodic time scale under the matched space (T,Π,F,δ) and n0N, the shift δτ(t) is Δ-differentiable with rd-continuous bounded derivatives δτΔ(t):=δΔ(τ,t) for all tT. A function fC(T×D,X) is called an n0-order Δ-almost periodic function (Δn0δ-almost periodic function) in tT uniformly for xD under the matched space if there exists some i01,niZ,i=1,2,,i0 such that the ε-shift set of Sfn1,ni0¯

E{ε,Sfn1,ni0¯,S}=τΠ˜:fδτ(t),xδτΔ(t)n0Sfn1,ni0¯(t,x)<ε,for all tT and xS

is a relatively dense set with respect to the pair (Π,δ˜) for all ε>0 and, for each compact subset S of D; that is, there exists some i01,niZ,i=1,2,,i0 such that for any given ε>0 and each compact subset S of D, there exists a constant l(ε,S)>0 such that each interval of length l(ε,S) contains a τ(ε,S)E{ε,Sfn1,ni0¯,S} such that

fδτ(t),xδτΔ(t)n0Sfn1,ni0¯(t,x)<ε,for all tT and xS,

where

Sfn1,ni0¯(t,x)=f(t,x)i=1i0δeΠΔ(t)ni.

Now, τ is called the ε-shift number of Sfn1,ni0¯ and l(ε,S) is called the inclusion length of E{ε,Sfn1,ni0¯,S}, and Sfn1,ni0¯ is called the approximation shift selection-function (ASS-function) of f.

In what follows, we established some basic properties of Δn0δ-almost periodic functions.

Theorem 39

([38]). Let fC(T×D,En) be Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0=f(t,x)δeΠΔ(t)n0 under the matched space (T,F,Π,δ), and δτ(t) is continuous in t. Then, Sfn0 is uniformly continuous and bounded on T×S.

In the following, we established a shift-convergence theorem of Δn0δ-almost periodic functions.

Theorem 40

([38]). Let fC(T×D,En) be Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0=f(t,x)δeΠΔ(t)n0 under the matched space (T,F,Π,δ). Then, for any given sequence αΠ˜, there exists a subsequence βα and gC(T×D,En) such that Tβδ,n0(Sfn0)=Sgn0 holds uniformly on T×S and g(t,x) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sgn0=g(t,x)δeΠΔ(t)n0 under the matched space (T,F,Π,δ).

Next, we give a sequentially compact criterion of Δn0δ-almost periodic functions through shift operator Tδ,n0.

Theorem 41

([38]). Let f(t,x)C(T×D,En). If for any sequence αΠ˜, there exists αα such that Tαδ,n0(Sfn0) exists uniformly on T×S, then f(t,x) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0 under the matched space (T,F,Π,δ), where Sfn0=f(t,x)δeΠΔ(t)n0.

From Theorems 40 and 41, we can obtain the following equivalent definition of uniformly Δn0δ-almost periodic functions.

Definition 46

([38]). Let f(t,x)C(T×D,En). If for any given sequence αΠ˜, there exists a subsequence αα such that Tαδ,n0(Sfn0) exists uniformly on T×S, where Sfn0=f(t,x)δeΠΔ(t)n0, then f(t,x) is called an Δn0δ-almost periodic function in t uniformly for xD with the ASS-function Sfn0 under the matched space (T,F,Π,δ).

Theorem 42

([38]). If f(t,x)C(T×D,En) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0=f(t,x)δeΠΔ(t)n0, φ(t) is Δn0δ-almost periodic with the ASS-function Sφn0=φ(t)δeΠΔ(t)n0 and {Sφn0:tT}S, then ft,Sφn0(t) is Δn0δ-almost periodic with the ASS-function

Sfn0=f(t,Sφn0(t))δeΠΔ(t)n0.

Definition 47

([38]). Let f(t,x)C(T×D,En). Then, Hn0(Sfn0)={Sgn0(t,x):TEn| there exists αΠ˜ such that Tαδ,n0Sfn0(t,x)=Sgn0(t,x) exists uniformly on T×S} is called the n0-order hull of Sfn0(t,x) under the matched space (T,F,Π,δ).

Theorem 43

([38]). Hn0(Sfn0) is compact if and only if f(t,x) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function f(t,x)δeΠΔ(t)n0.

Theorem 44

([38]). If f(t,x) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0=f(t,x)δeΠΔ(t)n0 under the matched space (T,F,Π,δ), then, for any Sgn0(t,x)Hn0(Sfn0), we have Hn0(Sfn0)=Hn0(Sgn0).

Now, we establish a sufficient and necessary criterion for Δn0δ-almost periodic functions.

Theorem 45

([38]). A function f(t,x) is Δn0δ-almost periodic in t uniformly for xD with the ASS-function Sfn0=f(t,x)δeΠΔ(t)n0 under the matched space (T,F,Π,δ) if and only if for every pair of sequences α,βΠ˜, there exist common subsequences αα,ββ such that

Tδ˜(α,β)δ,n0Sfn0(t,x)=Tαδ,n0Tβδ,n0Sfn0(t,x).

In [38], the linear Δn0δ-almost periodic dynamic equation on T was discussed:

xΔ=SAn0(t)x(t)+Sfn0(t) (13)

and its associated homogeneous equation

xΔ=SAn0(t)x(t), (14)

where A(t) is an Δn0δ-almost periodic matrix function and f(t) is an Δn0δ-almost periodic vector function.

Theorem 46

([38]). Let A(t) be an Δn0δ-almost periodic matrix function with the ASS-function SAn0 and f(t) be an Δn0δ-almost periodic vector function with the ASS-function Sfn0. If (14) admits an exponential dichotomy, then (13) has a unique δ-almost periodic solution with the Δn0δ-almost periodic function x:

Sxn0(t)=tSXn0(t)PSX1n0(σ(s))Sfn0(s)Δst+SXn0(t)(IP)SX1n0(σ(s))Sfn0(s)Δs,

where SXn0(t) is the fundamental solution matrix of (14) and X(t) is the fundamental matrix solution for xΔ(t)=A(t)x(t).

As an application of Theorem 46, the following almost periodic dynamic equation with variable delays under the matched space (T,F,Π,δ) was considered:

xΔ(t)=SAn0(t)x(t)+i=1nSfn0t,xδ(τi(t),t), (15)

where A(t) is an Δn0δ-almost periodic matrix function on T, τi(t):TΠ is Δn0δ-almost periodic on T for every i=1,2,,n, fC(T×Rn,Rn) is Δn0δ-almost periodic uniformly in t for xRn.

Theorem 47

([38]). Suppose that the following hold:

  • (H1)

    xΔ(t)=SAn0(t)x(t) admits an exponential dichotomy on T with positive constants K and α.

  • (H2)

    There exists M<α2Kn such that |Sf(t,x)Sf(t,y)|M|SxSy| for tT,x,yRn.

Then, the system (15) has a unique δ-almost periodic solution with the Δn0δ-almost periodic affiliated function.

3. The Uncertainty Theory on Time Scales with Shift Operators

As is known to all that all kinds of natural changes are full of uncertainty. To describe this inaccuracy in an accurate way, the stochastic theory and fuzzy theory are always applied to overcome these difficulties in physics and biological field (see [42,43,44,45]), etc.

In this section, we will present some recent main results of the stochastic and fuzzy dynamic equations on translational and non-translational time scales. Non-translational time scales are always with shift operators introduced in [46]. Some new equivalent concepts of the periodic time scales in shift operators were proposed in [47,48,49,50] to establish the theory of almost periodic and almost automorphic functions on irregular time scales.

3.1. The Stochastic Theory on Time Scales

The theory of stochastic dynamic equations was discussed in [51] and applied to study the existence and exponential stability of piecewise mean-square almost periodic solutions of the impulsive stochastic Nicholson’s blowflies model on time scales (see [15]).

Let (Ω,F,P) be a probability space and L2(Rn) stands for a space that consists of all Rn-valued random variables x with the norm

Ex2=Ωx2dP.

Let ω be a standard Wiener process and suppose {ω(t+h)ω(t):h0} is independent of Ft:=σ{ω(s):0st}, where FR:={Ft:tR} is a filtration on R, and with σ{·}, we mean the σ-algebra generated by {·}. We denote Δ-stochastic integral on [0,1]T, by 01f(t)Δω(t).

Lemma 2

([51]). The Δ-stochastic integral has the following properties:

  • (i)
    If f1,f2L2([0,1]T) and c1,c2R, then
    01c1f1(t)+c2f2(t)Δω(t)=c101f1(t)Δω(t)+c201f2(t)Δω(t).
  • (ii)
    If E01|f(t)|2Δt<, then E01f(t)Δω(t)=0 and the Itô-isometry holds, i.e.,
    E01f(t)Δω(t)2=E01f2(t)Δt.

Definition 48

([46]). Let T be a time scale with the shift operators δ± associated with the initial point t0T. The time scale T is said to be periodic in shifts δ± if there exists a p(t0,)T such that (p,t)D for all tT. Furthermore, if

P:=infp(t0,)T:(p,t)Dfor all tT{t0},

then P is called the period of the time scale T, where D±=(s,t)[t0,)T×T:δ(s,t)T.

Based on Definition 48, we introduce the following concept of relatively dense set under periodic time scales with shifts δ±.

Definition 49

([47,48]). Let T be a time scale with the shifts operators δ± associated with the initial point t0T. A subset S of R is called relatively dense under the shift δ+ if there exists a positive number L(t0,)T such that [a,δ+(L,a)]TS for all aT. The number L is called the inclusion length with respect to the pair (T,δ+).

Remark 3.

In fact, some classical definitions of relatively dense set from Definition 49 can be addressed below.

  • (i)

    Let T=R,δ+(L,a)=a+L. Definition 49 can be written as:

    Definition 50.
    A subset S of R is called relatively dense if there exists a positive number L such that [a,a+L]S for all aR.
  • (ii)

    Let T=qZ¯,q>1,δ+(L,a)=aL, Definition 49 is equivalent to the notion of relatively dense set on quantum time scale:

    Definition 51.
    A subset S of R is called relatively dense if there exists a positive number L(1,)qZ such that [a,aL]qZS for all aqZ.
  • (iii)

    Let T=N12,δ+(L,a)=L2+a2. The concept of relatively dense set on this irregular time scale follows immediately:

    Definition 52.
    A subset S of R is called relatively dense if there exists a positive number L(0,)N12 such that [a,L2+a2]N12S for all aN12.
  • (iv)

    Let T=Z,δ+(L,a)=a+L. The concept of relatively dense set in discrete situation can be stated as follows:

    Definition 53.
    A subset S of R is called relatively dense if there exists a positive number L(0,)Z such that [a,a+L]ZS for all aZ.

From (i),(ii),(iii),(iv), it easily follows that Definition 49 is efficient and feasible to cover some important irregular time scales. Based on it, the almost periodic functions on irregular time scales can be introduced.

For convenience, PCrdT,L2(Rn) denotes the set of all piecewise continuous stochastic process with respect to a sequence {tk},kZ.

By Lemma 1 from [46], the following lemma follows.

Lemma 3

([47,48]). If tkj=δ(tk,tk+j) and k,jZ, then

δ(tkj,tk+k1j)=δ(tkk1,tk+jk1),δ(tkk1,tkj)=tk+k1jk1.

According to Lemma 3, we adopt the notion tkj:=δ(tk,tk+j) and introduce the concept of equipotentially almost periodic sequence under the shifts operators δ±.

Definition 54

([47,48]). For any ε>0, let ΓεT be a set of real numbers and {tk}T. We say {tkj},k,jZ is equipotentially almost periodic under the shifts operators δ± if for rΓε, there exists at least one integer q such that |tkqr|<ε,for all kZ.

Based on Definition 49, we can introduce the following new concepts of almost periodic stochastic process. Let ΩL2(Rn) or Ω=L2(Rn), we will introduce the following definitions.

Letting t0 be the initial point and Π:=pT:(p,t)D±for all tT{t0},, then for any sΠ, we define a function A:ΠΠ,

A(s)=δ+(s,t0),s>t0,δ(s,t0),s<t0,

which will be used later. Note that A(s)>t0 and A(s)s.

Definition 55

([47,48]). Let T be periodic in shifts δ± and t0T be an initial point. {tk}T satisfies that the derived sequence {tkj},k,jZ, is equipotentially almost periodic under the shifts operators δ±. We call a stochastic process φPCrdT×Ω,L2(Rn) mean-square almost periodic in t uniformly for xΩ if for any ε>0 and for each compact subset S of Ω:

  • (i)

    there is a positive number δ=δ(ε,S) such that if the points t and t belong to the same interval of continuity and Aδ(t,t)<δ, then Eφ(t,x)φ(t,x)2<ε for all t,tT;

  • (ii)

    there is relative dense set Γ0(t0,)T of mean-square ε-almost periods with respect to the pair (T,δ+) such that if τΓ0, then Eφδ+(τ,t),xφ(t,x)2<ε for all (t,x)T×S which satisfies the condition Aδ(t,tk)>ε,kZ.

In 2017, Wang and Agarwal firstly proposed the concept of relatively dense set under time scales with shift operators and established the following basic notions and properties to investigate the almost periodicity and almost automorphy of impulsive dynamic equations on more general hybrid time scales (see [47,49]).

Let

D±=(s,t)T×T:δ±(s,t)T.

For any sT, denote

Tδs:=δ(s,T):=δ(s,t):(s,t)D,tT, (16)
Tδs+:=δ+(s,T):=δ+(s,t):(s,t)D+,tT. (17)

Definition 56

([50]). Let T be a time scale attached with the shifts operators δ± and t0T is the initial point. The time scale T is called bi-direction shift complete-closed time scales (or S-CCTS for short) in shifts δ± if

Π:=pT:(p,t)D±for all tT{t0},. (18)

By (16) and (17), we may rewrite (18) into the equivalent form Π=pT:Tδp±T{t0},.

Furthermore, from (18), we will refine the following the concept of S-CCTS attached with shift direction. For convenience, we will use the notations

Π+:=pT:TδpT,Π:=pT:TδpT.

Definition 57

([50]). Let T be an S-CCTS. Then,

  • (i)

    we say S-CCTS is with positive-direction if Π+{t0},;

  • (ii)

    we say S-CCTS is with negative-direction if Π{t0},;

  • (iii)

    we say S-CCTS is with bi-direction if Π{t0},.

Through Definitions 49 and 55, the authors investigated the almost periodic oscillations for delay impulsive stochastic Nicholson’s blowflies timescale model and the almost periodic dynamical behavior of a new type of neutral impulsive stochastic Lasota-Wazewska timescale model, respectively.

In [48], two new concepts of mean-square almost periodic stochastic processes were first introduced and the following timescale model was considered:

Δxi(t)+ci(t)xi(δ(τi,t))=αi(t)xi(t)+j=1mβij(t)eγij(t)xj(δ(τij,t))Δt+j=1mHijt,xj(δ(σij,t))Δωj(t),ttk,Δ˜xi(tk)=xi(tk+)xi(tk+)=Iik(xi(tk))+αikxi(tk)+νik,t=tk, (19)

where xi denotes the number of the red blood cells at time t of the ith animal, ci(t) is the stimulative rate of the generation of red blood cells per unit time, and τi is the stimulative time needed to produce blood cells of the ith animal. αi is the rate of death of the red blood cells of the ith animal, βij and γij describe the generation of red blood cells per unit time and τij is the time needed to produce blood cells of the ith animal when blood of the jth animal is transfused into the ith one. Δxi(t) denotes a Δ-stochastic differential of xi(t), αi,βij,γijPCrd(T,R+), τi,τij,σij are some positive constants, {tk}B, B={tk}:tkT,tk<tk+1,kZ,limk±=±, the constants αik,νikR and IikC(L2(R),R), Hij is Borel measurable, i=1,2,,n,j=1,2,,m,kZ and A=(Hij)n×m is a diffusion coefficient matrix (i.e., the random perturbation term for the system). The operator δ±:TT are shifts operators satisfying all the conditions in Definition 3 from [46] (here T¯=T, T¯ denotes the closure of T, i.e, T is the largest subset of T). Let (Ω,F,P) be a complete probability space furnished with a complete family of right continuous increasing sub σ-algebras {Ft:t[0,+)T} satisfying FtF. ω(t)=ω1(t),ω2(t),,ωm(t) is an m-dimensional standard Brownian motion over (Ω,F,P). Some sufficient conditions are obtained ensuring the existence of mean-square almost periodic solutions for system (19) by inverse operator theorem and fixed point theorem.

The following result concerning the existence of square-mean positive almost periodic solutions for (19) was established in [48].

Theorem 48

([48]). If the conditions (A1)(A4) are fulfilled—if (A5) holds, i.e, the following inequalities holds:

3K2λ2{211cM2i=1nj=1mβijMγijM2+i=1nαiMciM2+i=1nj=1mlij121ciM2}+3K21eλ(θ,0)2i=1nLi12(1+ciM)1ciM2<1,then

there exists a unique piecewise mean-square almost periodic solution x(t) of system (19) in the region B=φ˜:φ˜PCrdT,L2(Rn),Eφ˜(t)2K01cM2,tT.

3.2. The Fuzzy Theory on Time Scales

Time scale theory is also a powerful tool in establishing the fuzzy theory on hybrid domains. Based on the Hilger theory, in [50], Wang, Agarwal, and O’Regan established the theory of calculus of fuzzy vector-valued functions and almost periodic fuzzy vector-valued functions on time scales.

Definition 58

([52,53]). Letting KCn be the space of nonempty compact convex set of Rn, A,BKCn, we define the generalized Hukuhara difference of A and B as the set CKCn such that

AgHB=C(I)A=B+Cor(II)B=A+(1)·C. (20)

In the following part, we establish an embedding theorem for fuzzy multidimensional space.

Definition 59

([50]). Let uiRF for each i=1,2,,n. We say u=(u1,u2,,un)RF×RF××RFn terms=×i=1n{RF}:=[RFn] is a fuzzy (box) vector, where ×i=1n denotes the Cartesian product.

Remark 4.

Let u=(u1,u2,,un)[RFn], then the α-level of u are multidimensional intervals (box) of Rn(see Section 3 from Stefanini [53]). In fact, a multidimensional interval (box) of Rn can be regarded as a fuzzy (box) vector.

Let u=(u1,u2,,un) and v=(v1,v2,,vn) be two fuzzy vectors with (box) α-levels:

[u]α=[u1,α,u1,α+]×[u2,α,u2,α+]××[un,α,un,α+]:=×i=1n[ui,α,ui,α+],
[v]α=[v1,α,v1,α+]×[v2,α,v2,α+]××[vn,α,vn,α+]:=×i=1n[vi,α,vi,α+].

The distance is defined by

D(u,v)=supα[0,1]max{i=1n|su(α,Pi)sv(α,Pi)|212,i=1n|su(α,Pi)sv(α,Pi)|212:α[0,1],Pi,PiSn1Vn1,i=1,2,,n}, (21)

and the distance D(·,·) induces ·F on [RFn] defined by uF=D(u,0˜), where 0˜=(0˜,0˜,,0˜) and 0˜ is a zero element of RF. In fact, because

su(α,Pi),su(α,Pi)=[ui,α,ui,α+],i=1,2,,n,
sv(α,Pi),sv(α,Pi)=[vi,α,vi,α+],i=1,2,,n,

then

[u˜gHv]α=[u]αgH[v]α=(i)×i=1n[sv(α,Pi)su(α,Pi),su(α,Pi)sv(α,Pi)]or(ii)×i=1n[su(α,Pi)sv(α,Pi),sv(α,Pi)su(α,Pi)],

so, from (21), we have

D(u,v)=supα[0,1]{[u]αgH[v]α}=u˜gHvF=supα[0,1]max{i=1n|su(α,Pi)sv(α,Pi)|212,i=1n|su(α,Pi)sv(α,Pi)|212:α[0,1],Pi,PiSn1Vn1,i=1,2,,n}.

Remark 5.

For each i=1,2,,n, if we introduce the distance

D(i)(ui,vi)=supα[0,1]max{|su(α,Pi)sv(α,Pi)|,|su(α,Pi)sv(α,Pi)|:α[0,1],Pi,PiSn1Vn1},

the distance D(i)(·,·) induces ·F0 on RF defined by uiF0=D(ui,0˜), and then it follows that

D(u,v)=u˜gHvF=i=1nD(i)(ui,vi)12=i=1nuiviF0212.

Theorem 49

([50]). The metric space ([RFn],D) is complete.

In addition, the following theorem can be proved immediately.

Theorem 50

([50]). ×i=1nC¯[0,1]×C¯[0,1], with the norm defined by

(f1,g1),(f2,g2),,(fn,gn)×i=1n(C¯×C¯)=supx[0,1]maxi=1nfi2(x)12,i=1ngi2(x)12

is a Banach space.

The embedding theorem was established as follows.

Theorem 51

(Embedding theorem of fuzzy multidimensional space, [50]). For all u[RFn], denote j(u)=×i=1nui,ui+. Then, j([RFn]) is a closed convex cone with vertex 0 in ×i=1nC¯[0,1]×C¯[0,1] and j:[RFn]×i=1nC¯[0,1]×C¯[0,1] satisfies:

  • (i)

    for all u,v[RFn], s^,t0, j(s^·u+˜t·v)=s^j(u)+tj(v);

  • (ii)

    D(u,v)=j(u)j(v)×i=1n(C¯×C¯);

i.e., j embeds [RFn] into ×i=1nC¯[0,1]×C¯[0,1] isometrically and isomorphically.

Next, six new types of multiplication of two compact intervals were introduced as follows.

Let [u,u+] and [v,v+] be two compact intervals and ab denote the ordinary product of real numbers a,b. For convenience, we introduce the following notations:

Iu,v(I)=uu+vv+,Iu,v(II)=u+uvv+,Iu,v(III)=uuvv+,
Iu,v(IV)=u+u+vv+,Iu,v(V)=uu+vv,Iu,v(VI)=uu+v+v+.

For any [a,a+][u,u+] and [b,b+][v,v+], we defined the following multiplications:

Type I.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (22)

where if Iu,v(I)0, then

ab=ab,ab[uv+,u+v],uv+,ab<uv+,u+v,ab>u+v;

if Iu,v(I)0, then

ab=ab,ab[u+v,uv+],u+v,ab<u+v,uv+,ab>uv+.
Type II.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (23)

where if Iu,v(II)0, then

ab=ab,ab[u+v+,uv],u+v+,ab<u+v+,uv,ab>uv;

if Iu,v(II)0, then

ab=ab,ab[uv,u+v+],uv,ab<uv,u+v+,ab>u+v+.
Type III.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (24)

where if Iu,v(III)0, then

ab=ab,ab[uv+,uv],uv+,ab<uv+,uv,ab>uv;

if Iu,v(III)0, then

ab=ab,ab[uv,uv+],uv,ab<uv,uv+,ab>uv+.
Type IV.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (25)

where if Iu,v(IV)0, then

ab=ab,ab[u+v,u+v+],u+v,ab<u+v,u+v+,ab>u+v+;

if Iu,v(IV)0, then

ab=ab,ab[u+v+,u+v],u+v+,ab<u+v+,u+v,ab>u+v.
Type V.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (26)

where if Iu,v(V)0, then

ab=ab,ab[uv,u+v],uv,ab<uv,u+v,ab>u+v;

if Iu,v(V)0, then

ab=ab,ab[u+v,uv],u+v,ab<u+v,uv,ab>uv.
Type VI.[a,a+][b,b+]=ab:a[a,a+],b[b,b+], (27)

where if Iu,v(VI)0, then

ab=ab,ab[uv+,u+v+],uv+,ab<uv+,u+v+,ab>u+v+;

if Iu,v(VI)0, then

ab=ab,ab[u+v+,uv+],u+v+,ab<u+v+,uv+,ab>uv+.

Now, six types of the multiplication of fuzzy vectors induced by the multiplications of compact intervals can be defined by (22)–(27). For any α[0,1] and i=1,2,,n, we introduce the notations:

Iui,viα,(I)=ui,αui,α+vi,αvi,α+,Iui,viα,(II)=ui,α+ui,αvi,αvi,α+,Iui,viα,(III)=ui,αui,αvi,αvi,α+,
Iui,viα,(IV)=ui,α+ui,α+vi,αvi,α+,Iui,viα,(V)=ui,αui,α+vi,αvi,α,Iui,viα,(VI)=ui,αui,α+vi,α+vi,α+,

then we define the following types IVI with the (compact box) α-level set:

Type I.[uv]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,αvi,α+,ui,α+vi,α]if Iui,viα,(I)0,[ui,α+vi,α,ui,αvi,α+]if Iui,viα,(I)0; (28)
Type II.[uv]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,α+vi,α+,ui,αvi,α]if Iui,viα,(II)0,[ui,αvi,α,ui,α+vi,α+]if Iui,viα,(II)0; (29)
Type III.[u^v]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,αvi,α+,ui,αvi,α]if Iui,viα,(III)0,[ui,αvi,α,ui,αvi,α+]if Iui,viα,(III)0; (30)
Type IV.[u^v]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,α+vi,α,ui,α+vi,α+]if Iui,viα,(IV)0,[ui,α+vi,α+,ui,α+vi,α]if Iui,viα,(IV)0; (31)
Type V.[u˜v]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,αvi,α,ui,α+vi,α]if Iui,viα,(V)0,[ui,α+vi,α,ui,αvi,α]if Iui,viα,(V)0; (32)
Type VI.[u˜v]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+],
where[ui,α,ui,α+][vi,α,vi,α+]=[ui,αvi,α+,ui,α+vi,α+]if Iui,viα,(VI)0,[ui,α+vi,α+,ui,αvi,α+]if Iui,viα,(VI)0. (33)

From Ref. [50], the interval multiplications (22)–(27) are well defined and have a well inclusion isotonicity, and so do (28)–(33) (see Remark 2.14 from [50]).

Remark 6.

For Iui,viα,(I)=0 for all i=1,2,,n, from (28), we have ui,αvi,α+=ui,α+vi,α, then

[uv]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+]=×i=1n{ui,αvi,α+}=×i=1n{ui,α+vi,α}.

Similarly, for Iui,viα,(II)=0 for all i=1,2,,n, from (29), we have

[uv]α=×i=1n[ui,α,ui,α+][vi,α,vi,α+]=×i=1n{ui,α+vi,α+}=×i=1n{ui,αvi,α},

noticing that ×i=1n[ai,ai]=×i=1n{ai} for any aiR. For example, given u=χ[a,a] and v=χ[b,b] in RF, where a,b>0, it follows that [u]α=[a,a], [v]α=[b,b] for all α[0,1]. Note that Iu,vα,(I)=Iu,vα,(II)=0, it indicates that [uv]α={ab} and [uv]α={ab}, i.e., uv=χ{ab} and uv=χ{ab}. In fact, it is easy to see that, if there exists some I^{I,II,,VI} such that Iui,viα,(I^)=0, then the corresponding product of α-levels defined by (28)(33) is a one-point set for Type I^.

Remark 7.

Since the interval multiplications defined by (22) and (27) have a well inclusion isotonicity, then (28) and (33) also has well inclusion isotonicity naturally. For example, given u=χ[1,0] and v=χ[1,1], then we have Iu,vα,(I)<0 for all α[0,1]. Therefore, uv is given by

[uv]α=[uα,uα+][vα,vα+]=[uαvα+,uα+vα]=[1,0]

for all α[0,1]. For any given a[1,0]=[uα,uα+] and b[1,1]=[vα,vα+], it implies that

ab=ab,ab[1,0],1,ab<1,0,ab>0,

which indicates that, for any [a,b][uα,uα+], [c,d][vα,vα+], we can obtain [a,b][c,d][uα,uα+][vα,vα+].

Remark 8.

Traditionally, the multiplication of compact intervals is induced by the ordinary multiplication of real numbers, i.e, for the real compact intervals U=[u,u+] and V=[v,v+], the interval C=[c,c+] defining the multiplication C=UV is given by

c=min{uv,uv+,u+v,u+v+},c+=max{uv,uv+,u+v,u+v+}.

In fact, C=UV={ab:aU,bV}. However, note that such a multiplication of compact intervals induced by ordinary multiplication of real numbers is completely different from the multiplications of compact intervals induced by ab above. In the example of Remark 7, given 12[uα,uα+], 14[vα,vα+], we have ab=(12)(14)=18[1,0]=[1,0][1,1] but (12)(14)=0[1,0]=[1,0][1,1].

Theorem 52

([50]). If u,v[RFn], then uvFuF·vF and uvFuF·vF.

From Theorem 51 and the definition embedding j, we can prove the following properties easily.

Theorem 53

([50]). For u,v,w[RFn], if the gH-difference among them exist, then the following properties hold:

  • (i)

    D(u±˜gHw,v±˜gHw)=D(u,v);

  • (ii)

    D(u±˜gHw,v±˜gHe)D(u,v)+D(w,e);

  • (iii)

    D(μ·u,μ·v)=|μ|D(u,v) for μR;

  • (iv)

    D(uw,vw)wFD(u,v) if (u˜gHv)ω=uω˜gHvω;

    D(uw,vw)wFD(u,v) if (u˜gHv)ω=uω˜gHvω;

  • (v)

    D(μ·u,ν·u)=|μν|uF for μ,ν0 or μ,ν0.

In this part, we will establish some basic results of calculus of fuzzy vector-valued functions on time scales.

For convenience, we introduce the following notations.

Let f,g:T[RFn], where f=(f1,f2,,fn), g=(g1,g2,,gn) with the box α-level sets (0α<1) as follows:

[f(t)]α=[f1,α(t),f1,α+(t)]×[f2,α(t),f2,α+(t)]××[fn,α(t),fn,α+(t)]=×i=1n[fi,α(t),fi,α+(t)]

and

[g(t)]α=[g1,α(t),g1,α+(t)]×[g2,α(t),g2,α+(t)]××[gn,α(t),gn,α+(t)]=×i=1n[gi,α(t),gi,α+(t)].

The following definition of the gH-Δ-derivative of fuzzy vector-valued functions on time scales was introduced to analyze the almost periodic fuzzy dynamic equations on time scales.

Definition 60

([50]). For f:T[RFn] and tTκ, we define the gH-Δ-derivative of f(t),fΔ(t)=(f1Δ,f2Δ,,fnΔ), to be the fuzzy vector (if it exists) with the property that for a given ε>0, there exists a neighborhood U of t (i.e., U=(tδ,t+δ)T for some δ>0) such that

D(i)fi(σ(t))˜gHfi(s),fiΔ(t)(σ(t)s)<ε|σ(t)s|,i=1,2,,n

for all sU. That is, the limit

fiΔ(t)=limstfiσ(t)˜gHfi(s)σ(t)s

exists for each i=1,2,,n.

The following definition is obviously equivalent to Definition 60.

Definition 61

([50]). For f:TRFn and tTκ, we define the gH-Δ-derivative of f(t),fΔ(t)=(f1Δ,f2Δ,,fnΔ), to be the fuzzy vector (if it exists) with the property that for a given ε>0, there exists a δ>0 such that |h|<δ implies

D(i)fi(σ(t))˜gHfi(t+h),fiΔ(t)(μ(t)h)ε|μ(t)h|,

i.e.,

limh0fiσ(t)˜gHfi(t+h)μ(t)h=fiΔ(t)

exists for each i=1,2,,n.

A sufficient and necessary condition for gH-Δ-differentiability of functions is given by the following theorem.

Theorem 54

([50]). Let f:TRFn be a function and [f(t)]α=×i=1n[fi,α(t),fi,α+(t)], α[0,1]. The function f(t) is gH-Δ-differentiable if fi,α(t) and fi,α+(t) are Δ-differentiable real-valued functions for each i=1,2,,n. Furthermore,

[fΔ(t)]α=×i=1nmin{(fi,α)Δ(t),(fi,α+)Δ(t)},max{(fi,α)Δ(t),(fi,α+)Δ(t)}.

By Theorem 54, for the definition of gH-Δ-differentiability, we distinguished two cases, corresponding to (I) and (II) of (20).

Definition 62

([50]). Let f:TRFn be a function and [f(t)]α=×i=1n[fi,α(t),fi,α+(t)], α[0,1]. Let fi,α(t) and fi,α+(t) be Δ-differentiable real-valued functions at t0(a,b)T for each i=1,2,,n and α[0,1]. We say that f is (I)-gH-Δ-differentiable at t0(a,b)T if fΔI(t)=f1ΔI(t),f2ΔI(t),,fnΔI(t) with α-level set

[fΔI(t)]α=×i=1n[(fi,α)Δ(t),(fi,α+)Δ(t)], (34)

and f is (II)-gH-Δ-differentiable at t0(a,b)T if fΔII(t)=f1ΔII(t),f2ΔII(t),,fnΔII(t) with α-level set

[fΔII(t)]α=×i=1n[(fi,α+)Δ(t),(fi,α)Δ(t)]. (35)

Similar to Ref. [53], we will introduce and study the switch between the two cases (I) and (II) in Definition 62.

Definition 63

([50]). We say a point t0(a,b)T is a switching point for the gH-Δ-differentiability of f, if, in any neighborhood U of t0, there exists points t1<t0<t2 such that

  • (i)

    (type-I switch) at t1(34) holds while (35) does not hold and at t2(35) holds while (34) does not hold, or

  • (ii)

    (type-II switch) at t1(35) holds while (34) does not hold and at t2(34) holds while (35) does not hold.

Theorem 55

([50]). If f,g:TRFn is gH-Δ-differentiable at tTk, then

  • (i)

    f(σ(t))=f(t)+˜μ(t)·fΔ(t) or f(t)=f(σ(t))+˜(1)μ(t)·fΔ(t), i.e., fσ(t)˜gHf(t)=μ(t)·fΔ(t).

  • (ii)
    Let f,g be (I)-gH-Δ-differentiable at t(a,b)T or (II)-gH-Δ-differentiable at t(a,b)T, then f+˜g:TRFn is gH-Δ-differentiable at t and
    (f+˜g)Δ=fΔ(t)+˜gΔ(t).
  • (iii)
    For any nonnegative constant λR, λ·f:TRFn is gH-Δ-differentiable at t with
    (λ·f)Δ(t)=λ·fΔ(t).

In the following, we examine the relations between gH-Δ-differentiability and the integral of fuzzy vector-valued functions on time scales.

Definition 64

([50]). The fuzzy Aumann Δ-integral (or Δ-integral for short) of f:[a,b]TRFn is defined level-wise by

abf(t)Δtα=abf(t)αΔt=×i=1nab[fi(t)]αΔt=×i=1nabfi(t)Δt,abfi+(t)Δt,α[0,1].

Some basic calculus results of fuzzy functions are established as follows.

Theorem 56

([50]). Let f:[a,b]TRFn be continuous with [f(t)]α=×i=1n[fi,fi+]α. Then,

  • (i)

    the function F(t)=atf(s)Δs is gH-Δ-differentiable and FΔ(t)=f(t);

  • (ii)

    the function F(t)=tbf(s)Δs is gH-Δ-differentiable and GΔ(t)=f(t);

Theorem 57

([50]). If f:[a,b]TRFn is Δ-integrable and c[a,b]T. Then,

abf(t)Δt=acf(t)Δt+˜cbf(t)Δt.

Theorem 58

([50]). Assume that function f is gH-Δ-differentiable with n switching points at ci, i=1,2,n, a=c0<c1<c2<<cn<cn+1=b and exactly at these points. Then,

f(b)˜gHf(a)=i=1nci1cifΔ(t)Δt˜gH(1)cici+1fΔ(t)Δt.

In addition,

abfΔ(t)Δt=i=1n+1f(ci)˜gHf(ci1),

where summation denotes standard fuzzy addition in this statement.

Through our multiplication, the formula of integration by parts of fuzzy functions can be derived below.

Theorem 59

([50]). Assume f,g:[a,b]TRFn are (I)-gH-Δ-differentiable and fg is also (I)-gH-Δ-differentiable. If there is no switching point in [a,b]T and Ifi,giα,(I)>0, Ifiσ,giΔIα,(I)>0, IfiΔI,giα,(I)>0 for each i=1,2,,n, then

abf(t)gΔI(t)Δt=f(b)g(b)˜gHf(a)g(a)˜gHIabfΔI(t)gσ(t)Δtor
abf(t)gΔI(t)Δt=abgσ(t)fΔI(t)Δt˜gHIIf(a)g(a)˜gHf(b)g(b).

By adopting determinant algorithm of the multiplication of fuzzy vectors, some arithmetic properties of the gH-Δ-derivatives of the product of two fuzzy vector-valued functions on time scales were obtained. For convenience, we adopt the notation f(σ(t))=fσ(t) in some statement.

Theorem 60

([50]). Let f,g be (I)-gH-Δ-differentiable, then

  • (i)
    if Ifi,giα,(I)<0, Ifiσ,giΔIα,(I)<0, IfiΔI,giα,(I)<0 and fg is (I)-gH-Δ-differentiable, then
    (fg)ΔI=fσgΔI+˜fΔIg.
  • (ii)
    if Ifi,giα,(I)<0, Ifiσ,giΔIα,(I)>0, IfiΔI,giα,(I)>0 and fg is (II)-gH-Δ-differentiable, then
    (fg)ΔII=fσgΔI+˜fΔIg.
  • (iii)
    if Ifi,giα,(II)<0, Ifiσ,giΔIα,(II)<0, IfiΔI,giα,(II)<0 and fg is (I)-gH-Δ-differentiable, then
    (fg)ΔI=fσgΔI+˜gfΔI.
  • (iv)
    if Ifi,giα,(II)<0, Ifiσ,giΔIα,(II)>0, IfiΔI,giα,(II)>0 and fg is (II)-gH-Δ-differentiable, then
    (fg)ΔII=fσgΔI+˜fΔIg.
  • (v)
    if Ifi,giα,(I)>0, Ifiσ,giΔIα,(I)>0, IfiΔI,giα,(I)>0 and fg is (I)-gH-Δ-differentiable, then
    (fg)ΔI=fσgΔI+˜fΔIg.
  • (vi)
    if Ifi,giα,(I)>0, Ifiσ,giΔIα,(I)<0, IfiΔI,giα,(I)<0 and fg is (II)-gH-Δ-differentiable, then
    (fg)ΔII=fσgΔI+˜fΔIg.
  • (vii)
    if Ifi,giα,(II)>0, Ifiσ,giΔIα,(II)>0, IfiΔI,giα,(II)>0 and fg is (I)-gH-Δ-differentiable, then
    (fg)ΔI=fσgΔI+˜gfΔI.
  • (viii)
    if Ifi,giα,(II)>0, Ifiσ,giΔIα,(II)<0, IfiΔI,giα,(II)<0 and fg is (II)-gH-Δ-differentiable, then
    (fg)ΔII=fσgΔI+˜fΔIg.

In Ref. [50], the authors established the calculus of fuzzy vector-valued functions to study the almost periodic fuzzy vector-valued functions on time scales.

Definition 65

([50]). Let T be a bi-direction S-CCTS and f:T×DRFn be continuous on T×D.

  • (i)
    A function fC(T×D,RFn) is calledshift almost periodicfuzzy vector-valued function in tT uniformly for xD with shift operators if the ε-shift number set of f
    E{ε,f,S0}=τΠ:Df(δ±(τ,t),x),f(t,x)<ε,for all tT and xS0
    is a relatively dense set with respect to the pair (Π,δ±) for all ε>0 and for each compact subset S0 of D; that is, for any given ε>0 and each compact subset S0 of D, there exists a constant l(ε,S0)>0 such that each interval of length l(ε,S0) contains a τ(ε,S0)E{ε,f,S0} such that
    Dfδ±(τ,t),x,f(t,x)<ε,for all tT and xS0.
    Now, τ is called the ε-shift number of f and l(ε,S0) is called the inclusion length of E{ε,f,S0}.
  • (ii)
    A function fC(T×D,RFn) is called shift normal function if for any sequence Fn:T×DRFn of the form Fn(t,x)=fδ+(hn,t),x,nN, where (hn)nΠ is a sequence of real numbers, one can extract a subsequence of (Fn)n, converging uniformly on T×D(i.e., (hn)nΠ, (hn)k, F:TRFn which may depend on (hn)n), such that
    DFnk(t,x),F(t,x)0 as k
    uniformly with respect to (t,x)T×D.
  • (iii)
    Let δ±(s,t) be Δ-differentiable to its second argument. A function fC(T×D,RFn) is called shift Δ-almost periodic fuzzy vector-valued function in tT uniformly for xD with shift operators if the ε-shift number set of f
    E{ε,f,S0}=τΠ:Df(δ±(τ,t),x)δ±Δ(τ,t),f(t,x)<ε,for all tT and xS0
    is a relatively dense set with respect to the pair (Π,δ±) for all ε>0 and for each compact subset S0 of D; that is, for any given ε>0 and each compact subset S0 of D, there exists a constant l(ε,S0)>0 such that each interval of length l(ε,S0) contains a τ(ε,S0)E{ε,f,S0} such that
    Dfδ±(τ,t),xδ±Δ(τ,t),f(t,x)<ε,for all tT and xS0.
    Now, τ is called the ε-shift number of f and l(ε,S0) is called the inclusion length of E{ε,f,S0}.
  • (iv)
    Let δ±(s,t) be Δ-differentiable to its second argument. A function fC(T×D,RFn) is called shift Δ-normal function if for any sequence Fn:T×DRFn of the form Fn(t,x)=fδ+(hn,t),xδ+Δ(hn,t),nN, where (hn)nΠ is a sequence of real numbers, one can extract a subsequence of (Fn)n, converging uniformly on T×D(i.e., (hn)nΠ, (hn)k, F:TRFn which may depend on (hn)n), such that
    DFnk(t,x),F(t,x)0 as k
    uniformly with respect to (t,x)T×D.

For convenience, we denote APS(T) the set of all shift almost periodic functions in shifts on T and we introduce some notation. Let α={αn}Π and β={βn}Π be two sequences. Then, βα means that β is a subsequence of α; δ±(α,β)={δ±(αn,βn)};δ(α,t0)={δ(αn,t0)}, α and β are common subsequences of α and β, respectively, means that αn=αn(k) and βn=βn(k) for some given function n(k).

We introduce the moving-operator TS, TαSf(t,x)=g(t,x) by

g(t,x)=limn+fδ+(αn,t),x

and is written only when the limit exists. The mode of convergence, e.g., pointwise, uniform, etc., will be specified at each use of the symbol.

In what follows, we establish some basic properties of S-almost periodic fuzzy vector-valued functions.

Theorem 61

([50]). Let T be a bi-direction S-CCTS with shifts δ± and fC(T×D,RFn) be S-almost periodic in t uniformly for xD, where δ+(τ,t) is continuous in t. Then, it is uniformly continuous and bounded on T×S0.

In the following, we obtained a shift-convergence theorem of S-almost periodic fuzzy vector-valued functions.

Theorem 62

([50]). Let fC(T×D,RFn) be S-almost periodic in t uniformly for xD under shifts δ±. Then, for any given sequence αΠ, there exists a subsequence βα and gC(T×D,RFn) such that TβSf(t,x)=g(t,x) holds uniformly on T×S0 and g(t,x) is S-almost periodic in t uniformly for xD under shifts δ±.

The concept of the S-hull of f(t,x) under shifts δ± was introduced related to fuzzy almost periodic functions on time scales.

Definition 66

([50]). Let fC(T×D,RFn). Then, HS(f)={g(t,x):T×DRFn| and there exists αΠ such that TαSf(t,x)=g(t,x) exists uniformly on T×S0} is called the S-hull of f(t,x) under shifts δ±.

Theorem 63

([50]). HS(f) is compact if and only if f(t,x) is S-almost periodic in t uniformly for xD.

Theorem 64

([50]). If fC(T×D,RFn) is S-almost periodic in t uniformly for xD under shifts δ±, then, for any g(t,x)HS(f),HS(f)=HS(g).

From Definition 66 and Theorem 64, one can directly obtain the following theorem.

Theorem 65

([50]). If fC(T×D,RFn) is S-almost periodic in t uniformly for xD under shifts δ±, then, for any g(t,x)HS(f), g(t,x) is S-almost periodic in t uniformly for xD under shifts δ±.

In what follows, a convergence theorem of S-almost periodic function sequences is established.

Theorem 66

([50]). If fnC(T×D,RFn),n=1,2, are S-almost periodic in t for xD, and the sequence {fn(t,x)} uniformly converges to f(t,x) on T×S0, then f(t,x) is S-almost periodic in t uniformly for xD.

Theorem 67

([50]). Let fC(T×D,RFn) and j be an embedding mapping in Theorem 51. Then,

  • (i)

    jf is continuous on T if and only if f is continuous on T.

  • (ii)

    jf is S-almost periodic if and only if f is S-almost periodic.

  • (iii)

    If f is gH-Δ-differentiable on T, then jf is Δ-differentiable on T and (jf)Δ(t)=jfΔ(t) for tT.

Theorem 68

([50]). If fC(T×D,RFn) is shift-Δ-almost periodic in t uniformly for xD under shifts δ±, denote

F(t,x)=t0tf(s,x)Δs,t0T,

then F(t,x) is S-almost periodic in t uniformly for xD under shifts δ± if and only if F(t,x) is bounded on T×S0, where S0 is any compact subset of D.

A sufficient and necessary criterion for S-almost periodic functions was established.

Theorem 69

([50]). A function fC(T×D,RFn) is S-almost periodic in t uniformly for xD under shifts δ± if and only if for every pair of sequences α,βΠ, there exist common subsequences αα,ββ such that

Tδ+(α,β)Sf(t,x)=TαSTβSf(t,x).

4. The Quaternion Theory on Time Scales

To represent spatial orientations and rotations of elements in three-dimensional space, quaternions provide a convenient mathematical notation. Particularly, an axis-angle rotation about an arbitrary axis is encoded by the unit quaternion. In computer graphics, computer vision, robotics, navigation, molecular dynamics, flight dynamics, orbital mechanics of satellites and crystallographic texture analysis, rotation, and orientation quaternions have wide applications (see [54,55,56,57]).

The study of quaternion dynamic equations is an interesting topic (see [58,59]). In [60], Wang and Li firstly obtained the Cauchy matrix and Liouville formula of the quaternion impulsive dynamic equations on time scales. In [61], nine questions were proposed and solved in the quaternion dynamic equations on hybrid time scales as follows:

  • (1)

    By Euler’s rotation theory, one can represent a ring rotation through a corresponding quaternion (see Figure 1). However, if a rotation depends on a hybrid time domain, i.e., the ring’s rotation is intermittent, it is reasonable to consider the quaternion-valued functions on a time scale. It is difficult to describe the intermittent rotation by using a quaternion-valued functions on time scales.

  • (2)

    The direction of many conveyances are controlled by the gyroscope, for example, plane, ship, rocket, etc. The process of their motion is based on a time scale if the gyroscope does not work continuously. How should the work process of the gyroscope controlled by a 2×2 quaternion dynamic equation be depicted? When does the phenomenon "Gimbal Lock" take place (see Figure 2)? What is expression form of the solution to such quaternion dynamic equations?

  • (3)

    It is very common to see some phenomena described by a 2×2 quaternion dynamic equations on time scales. For example, in the process of a car going up a slope, the time that is consumed for changing the direction of the car can be regarded as a time scale which is located in the time interval from the bottom to the top of the hill (see Figure 3). It is convenient to use a 2×2 quaternion dynamic equations on a time scale to accurately describe the orientations and rotations of the car on the slope. How can a 2×2 quaternion dynamic equations to describe the process of the orientations and rotations of this car be established? What is the representation form of the solution to this dynamic equations?

  • (4)
    For the dynamic equation xΔ(t)=f(t)x(t) with the initial value x(t0)=1. The quaternion exponential function
    Ef(t,s)=expstξμ(τ˜)(f(τ˜)Δτ˜
    from the previous literature is not a solution, this deficiency will lead to a great difficulty to analyze some practical and theoretical problems. For example, the rocket will deviate from its intended route (see Figure 4). Therefore, it is urgent to find the quaternion exponential solution of this initial-valued problem.
  • (5)

    As is well known, three rings of the gyroscope work simultaneously such as warplane, rocket (see Figure 5), etc. Unfortunately, it is impossible to depict the orientations and rotations by a 2×2 quaternion dynamic equations for this case. Hence, it is necessary to consider the higher dimensional matrix quaternion dynamic equations. The main problem is how to establish some basic results of the 2×2 quaternion dynamic equations based on the double determinant algorithm and extend the case to n×n situation?

  • (6)

    Does the linear homogeneous n×n quaternion dynamic equations have a unique solution on time scales? What form does it have? In fact, many objects’ orientations and rotations can be described by n×n quaternion dynamic equations. If the solution is not unique, some reality problems will emerge such as losing the direction of the objects or suffering from the unexpected orientations and rotations.

  • (7)

    Letting X(t) be a solution of XΔ(t)=A(t)X(t) and Y(t) be a solution of YΔ(t)=B(t)Y(t), what are the commutativity conditions of X(t) and Y(t) on time scales? Moreover, what is the connection between the quaternion functions with commutativity conditions and the complex-valued function? What are the commutativity conditions of the quaternion-valued functions on time scales?

  • (8)

    Based on the double determinant algorithm, what is the Liouville formula QTDE(t) of the 2×2 linear homogenous quaternion dynamic equations on time scales? Particularly for QTDE(t)=0, what kind of the orientations and rotations phenomena will occur?

  • (9)

    We will encounter many problems in real applications in which the 2×2 or 3×3 quaternion dynamic equations are not sufficient. Taking the launching rocket as an example, the process will be affected by many factors, for example, the continuously changing earth gravity, the irregular wind power, the predictable and irregular air temperature and the continuously changing atmospheric pressure, etc. All these factors indicate that we must adopt the n×n quaternion dynamic equations on time scales. Therefore, some mathematical questions arise, such as what is the solution expression of the n×n quaternion dynamic equations XΔ(t)=Φ(t)X(t)? Do these dynamic equations have a unique solution? How can the Liouville formula of the n×n quaternion dynamic equations on time scales be obtained?

Figure 1.

Figure 1

The quaternion number and the rotation of the corresponding ring.

Figure 2.

Figure 2

The phenomenon “Gimbal Lock”.

Figure 3.

Figure 3

The 2×2 quaternion dynamic equations and the corresponding automobilism.

Figure 4.

Figure 4

The obstacle of the presented quaternion exponential function application.

Figure 5.

Figure 5

The 3×3 quaternion dynamic equations and the corresponding working diagram of a warplane.

4.1. Basic Results of Quaternion Dynamic Equations on Time Scales

In [61], the two-dimensional linear homogenous quaternion dynamic equations on time scales (or short for TQDEs) with the initial value were considered as follows:

hΔ(t)=Φ(t)h(t),h(t0)=h0H2, (36)

i.e.,

h1Δ(t)h2Δ(t)=p11(t)p12(t)p21(t)p22(t)h1(t)h2(t),

where Φ(·):TH2×2 is an rd-continuous quaternion-valued function on T.

The following Liouville formula for (36) through double determinant algorithm was established.

Theorem 70

(Liouville Formula, [61]). If τ is regressive for any tT, then the Wronskian QTDE(t) of (36) satisfies the following quaternion Liouville formula:

QTDE(t)=eτ(t,t0)QTDE(t0),

where

τ(t)=trΦ(t)+trΦ+(t)+trΦ(t)trΦ¯(t)+detrΦ(t)+detrΦ¯(t)μ(t)+detdΦ(t)μ3(t)+(p11(t)detrΦ¯(t)+detrΦ(t)p11¯(t)+p22(t)detcΦ¯(t)+detcΦ(t)p22¯(t))μ2(t)

and

trΦ(t)=p11(t)+p22(t),trΦ+(t)=p11¯(t)+p22¯(t),
detrΦ=p11(t)p22(t)p12(t)p21(t),detcΦ(t)=p11(t)p22(t)p21(t)p12(t).

Definition 67

([61]). Let A(·):THn×m, where A(t)=[awv(t)]n×m, 1wn, 1vm. If every awv(t) is rd-continuous, then A(t) is said to be an rd-continuous quaternion-valued matrix function.

Definition 68

([61]). Let A(t),B(t) be n×n-quaternion-valued matrix function, A(t) and B(t) are rd-continuous on T, and define derivatives

AΔ(t)=awvΔ(t)1w,vn,BΔ(t)=bwvΔ(t)1w,vn.

Define the “circle plus" addition as:

A(t)B(t)=A(t)+B(t)+μ(t)A(t)B(t).

Definition 69

([61]). Let f:TH. We define the quaternion exponential function ef(t,t0) by the solution of the initial value problem xΔ(t)=f(t)x(t),x(t0)=1, and ef(t,t0) can be given as

ef(t,t0)=1+n=1+t0tf(tn)t0tnf(tn1)t0t2f(t1)Δt1Δtn1Δtn.

Similarly, let Φ:THn×n. The quaternion matrix exponential function eΦ(t,t0) is defined by the solution of the initial value problem HΔ(t)=Φ(t)H(t),H(t0)=I, where I is n×n-identity matrix, and eΦ(t,t0) can be given as

eΦ(t,t0)=I+n=1+t0tΦ(tn)t0tnΦ(tn1)t0t2Φ(t1)Δt1Δtn1Δtn.

Consider the n-dimensional linear homogenous TQDEs with the initial value as follows:

hΔ(t)=Φ^(t)h(t),h(t0)=h0Hn, (37)

where Φ^(·):THn×n is an rd-continuous quaternion n×n-matrix function on T.

Theorem 71

([61]). If Φ^(t) is uniformly bounded on T, i.e., there exists some constant M>0 such that Φ^(t)M for all tT, then the solution h(t) of the initial value problem of (37) is rd-continuous and uniquely given by

h(t)=I+n=1t0tΦ^(tn)t0tnΦ^(tn1)t0t2Φ^(t1)Δt1Δtn1Δtnh0.

In the following, we provide a numerical iteration method of the linear homogenous three-dimensional TQDEs on the time scale T=2Z¯.

Example 2.

Let T=2Z¯, t[210,25], the linear homogenous three-dimensional TQDEs with the initial value as follows:

hΔ(t)=sint2+isint+jsin2t+kcost3cost+isin(t+1)+jcost+ksintksintsin2t+3i+2j+ksintsin4t+4i+jjsint1+4i+jcost+ksintsint+jsin2t+3kksint2h(t), (38)

with the initial value h(210)=[1,1,1]T, where h(t)=[h11(t)+h12(t)i+h13(t)j+h14(t)k,h21(t)+h22(t)i+h23(t)j+h24(t)k,h31(t)+h32(t)i+h33(t)j+h34(t)k]T and hΔ(t)=A+Bi+Cj+Dkh(t). The numerical solution of (38) can be solved by the following MATLAB code:

clear 
syms h11 h21 h31 h12 h22 h32 h13 h23 h33 h14 h24 h34 t; 
h11=1;h21=1;h31=1;h12=0;h22=0;h32=0;h13=0;h23=0;h33=0;h14=0;h24=0;h34=0; 
for n=-10:1:4;t=2.^n; 
h=[h11 h21 h31;h12 h22 h32;h13 h23 h33;h14 h24 h34]; 
A=[sin(t.^2) cos(t) 0;sin(2.*t) sin(4.*t) 0;1 sin(t) 0]’; 
B=[sin(t) sin(t + 1) 0;3 4 0;4 0 0]’; 
C=[sin(2.*t) cos(t) 0;2 1 sin(t);cos(t) sin(2.*t) 0]’; 
D=[cos(t.^3) sin(t) sin(t);sin(t) 0 0;sin(t) 3 sin(t.^2)]’; 
h=t.*[h(1,:)*A-h(2,:)*B-h(3,:)*C-h(4,:)*D;h(2,:)*A + h(1,:)*B + h(4,:)*C-h(3,:)*D; 
    h(3,:)*A-h(4,:)*B + h(1,:)*C + h(2,:)*D;h(4,:)*A + h(3,:)*B-h(2,:)*C + h(1,:)*D] + h 
end 
         

The numerical iteration solution of (38) is given by Table 1. Notice that the existence of solutions to quaternion homogeneous dynamic equations on time scales provides a prerequisite to study the applications of quaternion dynamic equations on various hybrid domains, these significant applications are demonstrated in [61] including the multi-dimensional rotations and transformations of the submarine, gyroscope and planet whose dynamical behaviors are depicted by quaternion dynamics on time scales.

Table 1.

The solution of (38).

t h11(t) h21(t) h31(t) h12(t) h22(t) h32(t) h13(t) h23(t) h33(t) h14(t) h24(t) h33(t)
9.7656 × 10−4 1 1 1 0 0 0 0 0 0 0 0 0
0.0020 1.0010 1.0000 1.0010 0.0008 0.0068 0.0039 0.0010 0.0029 0.0010 0.0010 0.0000 0.0029
0.0039 1.0020 1.0000 1.0020 0.0016 0.0137 0.0078 0.0020 0.0059 0.0020 0.0020 0.0000 0.0059
0.0078 1.0039 1.0001 1.0039 0.0033 0.0273 0.0156 0.0039 0.0117 0.0039 0.0039 0.0000 0.0117
0.0156 1.0078 1.0004 1.0079 0.0067 0.0547 0.0313 0.0079 0.0235 0.0079 0.0079 0.0001 0.0235
0.0313 1.0156 1.0015 1.0159 0.0135 0.1094 0.0625 0.0161 0.0471 0.0161 0.0161 0.0002 0.0471
0.0625 1.0313 1.0058 1.0322 0.0278 0.2188 0.1250 0.0332 0.0947 0.0332 0.0332 0.0010 0.0948
0.1250 1.0626 1.0233 1.0664 0.0585 0.4375 0.2500 0.0702 0.1914 0.0702 0.0703 0.0039 0.1916
0.2500 1.1260 1.0909 1.1406 0.1284 0.8750 0.5000 0.1550 0.3906 0.1550 0.1562 0.0156 0.3925
0.5000 1.2578 1.3302 1.3119 0.2991 1.7500 1.0000 0.3621 0.8119 0.3621 0.3737 0.0619 0.8275
1 1.5625 1.8754 1.7397 0.7385 3.5000 2.0000 0.8595 1.7397 0.8595 0.9755 0.2397 1.8634
2 2.3818 1.1525 2.8415 1.7508 7.0000 4.0000 1.4496 3.8415 1.4496 2.2232 0.8415 4.6829
4 −1.3459 1.4651 4.8186 2.1008 14.0000 8.0000 −2.3459 7.8186 −2.3459 3.3462 1.8186 6.3050
8 −2.7662 3.8058 1.9728 −6.8629 28.0000 16.0000 1.3429 8.9728 1.3429 −4.4870 −3.0272 7.8212
16 7.1962 3.1082 16.9149 11.2118 56.0000 32.0000 −3.4672 31.9149 −3.4672 7.8551 7.9149 39.2751
32 −30.3099 24.5432 12.3935 −19.9888 112.0000 64.0000 −6.4997 43.3935 −6.4997 3.6509 −4.6065 27.4062
step t h1(t) h2(t) h3(t)
0 9.7656 × 10−4 1 1 1
1 0.0020 1.0010 + 0.0008i + 0.0010j + 0.0010k 1.0000 + 0.0068i + 0.0029j + 0.0000k 1.0010 + 0.0039i + 0.0010j + 0.0029k
2 0.0039 1.0020 + 0.0016i + 0.0020j + 0.0020k 1.0000 + 0.0137i + 0.0059j + 0.0000k 1.0020 + 0.0078i + 0.0020j + 0.0059k
3 0.0078 1.0039 + 0.0033i + 0.0039j + 0.0039k 1.0001 + 0.0273i + 0.0117j + 0.0000k 1.0039 + 0.0156i + 0.0039j + 0.0117k
4 0.0156 1.0078 + 0.0067i + 0.0079j + 0.0079k 1.0004 + 0.0547i + 0.0235j + 0.0001k 1.0079 + 0.0313i + 0.0079j + 0.0235k
5 0.0313 1.0156 + 0.0135i + 0.0161j + 0.0161k 1.0015 + 0.1094i + 0.0471j + 0.0002k 1.0159 + 0.0625i + 0.0161j + 0.0471k
6 0.0625 1.0313 + 0.0278i + 0.0332j + 0.0332k 1.0058 + 0.2188i + 0.0947j + 0.0010k 1.0322 + 0.1250i + 0.0332j + 0.0948k
7 0.1250 1.0626 + 0.0585i + 0.0702j + 0.0703k 1.0233 + 0.4375i + 0.1914j + 0.0039k 1.0664 + 0.2500i + 0.0702j + 0.1916k
8 0.2500 1.1260 + 0.1284i + 0.1550j + 0.1562k 1.0909 + 0.8750i + 0.3906j + 0.0156k 1.1406 + 0.5000i + 0.1550j + 0.3925k
9 0.5000 1.2578 + 0.2991i + 0.3621j + 0.3737k 1.3302 + 1.7500i + 0.8119 j + 0.0619jk 1.3119 + 1.0000i + 0.3621j + 0.8275k
10 1 1.5625 + 0.7385i + 0.8595j + 0.9755k 1.8754 + 3.5000i + 1.7397j + 0.2397k 1.7397 + 2.0000i + 0.8595j + 1.8634k
11 2 2.3818 + 1.7508i + 1.4496j + 2.2232k 1.1525 + 7.0000i + 3.8415j + 0.8415k 2.8415 + 4.0000i + 1.4496j + 4.6829k
12 4 −1.3459 + 2.1008i− 2.3459j + 3.3462k 1.4651 + 14.0000i + 7.8186j + 1.8186k 4.8186 + 8.0000i− 2.3459j + 6.3050k
13 8 −2.7662 − 6.8629i + 1.3429j− 4.4870k 3.8058 + 28.0000i + 8.9728j− 3.0272k 1.9728 + 16.0000i + 1.3429j + 7.8212k
14 16 7.1962 + 11.2118i− 3.4672j + 7.8551k 3.1082 + 56.0000i + 31.9149j + 7.9149k 16.9149 + 32.0000i− 3.4672j + 39.2751k
15 32 −30.3099 − 19.9888i− 6.4997j + 3.6509k 24.5432 + 112.0000i + 43.3935j− 4.6065k 12.3935 + 64.0000i− 6.4997j + 27.4062k

Next, we will introduce a new Liouville algorithm of n×n quaternion-valued matrix which is an extension of the double determinant algorithm.

Definition 70

([61]). Let M be a n×n quaternion matrix, we define the Liouville algorithm of M by

Lioudn(M):=w=1nv=w+1ndetrM¯wTM¯vTMwMv=w=1nv=w+1ndetrc=1nm¯cwmcwc=1nm¯cwmcvc=1nm¯cvmcwc=1nm¯cvmcv,

where M=[M1,M2,,Mn]=[mwv]n×n.

By Definition 70, the following conclusion is immediate.

Remark 9.

Lioudn(M)=detd(M) for n=2.

Next, we will show the Liouville algorithm of the n×n quaternion-valued matrix is well-defined, i.e., Lioudn(M) is real.

Theorem 72

([61]). Let M be a n×n quaternion matrix, M=[Mwv]n×n, n2, then Lioudn(M)R.

Now, we will prove the Liouville formula of the linear homogenous n×n quaternion dynamic equations based on the fundamental matrix solution M(t) as follows.

Consider the n×n linear homogenous matrix TQDEs with the initial value as follows:

HΔ(t)=Φ^(t)H(t),H(t0)=H0Hn×n. (39)

Theorem 73

([61]). The Wronskian of (39) can be given as

QTDEn(t)=w=1nv=w+1nc=1nh¯cw(t)hcw(t)c=1nh¯cv(t)hcv(t)c=1nh¯cw(t)hcv(t)c=1nh¯cv(t)hcw(t).

4.2. Applied Quaternion Dynamic Equations

In Ref. [61], some real applications of the quaternion dynamic equations were demonstrated as follows.

In a three-dimensional case, Euler’s rotation theory demonstrates that any rotation can be represented as a combination of a scalar θ (called the Euler angle) and a vector e (the direction vector of Euler axis) (see Figure 6a), which indicates that we can regard a quaternion number as the result of a point that is described by the shift of a vector e which starts at the origin of R3 and the Euler angle θ which moves round e, i.e., we can define qH as q=q(θ,e). In a similar way, one can define the quaternion-valued matrix function Φ^(t) by

Φ^(t)=q11θ11(t),e11(t)q12θ12(t),e12(t)q1nθ1n(t),e1n(t)q21θ21(t),e21(t)q22θ22(t),e22(t)q2nθ2n(t),e2n(t)qm1θm1(t),em1(t)qm2θm2(t),em2(t)qmnθmn(t),emn(t)m×n.

Figure 6.

Figure 6

The diagram of the Euler’s rotation principle.

Consider the rotation of a circular ring, there are two approaches to form this rotation, i.e., rotate r(θ,e) to r1(θ1,e1) or to r2(θ2,e2) (see Figure 6b), which implies that we can represent the result of difference between two quaternion numbers as the rotation of a circular ring. Moreover, we can consider a quaternion dynamic equation

hΔ(t)=a(t)h(t),wherea:TH

with the initial value h(t0)=r(θ,e) to track the rotation that is from r(θ,e) to r1(θ1,e1).

Next, some further results will be shown on the rotation of gyroscope. For the gyroscope, we shall consider this rotation in an ideal state with the rotations α(Roll), β(Pitch) and γ(Yaw)(see Figure 7). Noticing that the rotation dynamical behavior of the gyroscope is dependent on the operation of the three related rings, we can describe the rotation of gyroscope by the quaternion dynamic equations

hΔ(t)=Φ^(t)h(t) (40)

with the initial value h(t0)=(h1(t0),h2(t0),h3(t0))T, where Φ^(t) is a 3×3 quaternion-valued matrix function, h1(t0) is the quaternion number corresponding to the initial state of the α(Roll)-axis, h2(t0) is the quaternion number corresponding to the initial state of the β(Pitch)-axis, h3(t0) is the quaternion number corresponding to the initial state of the γ(Yaw)-axis. Indeed, the dynamical behavior of the submarine can be represented by the rotation of gyroscope (see Figure 8). Moreover, let e0=(0,0,0), e1=(0,0,1), e2=(1,0,0),

Φ^(t)=q1(θ1(t),e0)0q3(θ3(t),e0)0q2(θ2(t),e0)0q1(θ1(t),e0)0q3(θ3(t),e0),

with the initial value h1(t0)=h3(t0)=h3(θ(t0),e1) and h2(t0)=h2(θ˜(t0),e2). Then, h1(t)=h3(t)=h3(θ(t),e1) and h2(t)=h2(θ˜(t),e2), a phenomenon of “Gimbal Lock” in Euler’s rotation principle indicates that there are two equivalent vector components in the vector solutions to the homogeneous equations (40). (see Figure 9). In the real applications, some monomer ships, including submarines, have a center of gravity and a center of buoyancy to maintain lateral stability, which indicates that we can consider the steering operation of submarines by the quaternion dynamic equations with the form (36).

Figure 7.

Figure 7

Initial state diagram of a submarine controlled by a gyroscope.

Figure 8.

Figure 8

A working diagram of a submarine controlled by a gyroscope.

Figure 9.

Figure 9

“Gimbal Lock”.

Time scale plays a powerful role in dealing with the current problems under the quaternion background. For example, the gyroscope will move from the state S1 to the state S2 by a continuous rotational force for T=R; it may be also subjected to a discontinuous rotational force for T={hZ} and then revoking the force on R{hZ}, by inertia, the gyroscope will move from the state S3 to the state S4 (see Figure 10). The similar cases will frequently occur on the quantum time scales T=qZ¯ and the hybrid time scales such as T={hZ}{qZ¯}, etc. All these problems belong to the quaternion problems on time scales.

Figure 10.

Figure 10

The gyroscope working diagram marked on different time scales.

Commutativity of the quaternion-matrix-valued functions is an important property. For instance, a rotation can be denoted by an Euler angle θ and a unit vector defined by

u=(ux,uy,uz)=uxi+uyj+uzk,

i.e., this rotation can be represented by a quaternion. In this paper, we have established some results of the commutativity of quaternion-valued functions. Based on it, two quaternion-valued functions can commutate with each other implies that the directional vectors of Euler axis are parallel to each other, which can contribute to studying the relationship between two particular status (or solutions) of the quaternion dynamic equations.

Another application is about the rotation of the planet. The rotation direction e1(t0) and the rotation angle θ1(t0) of the planet α at time t0 describe the space state of the planet α at t0, i.e., a quaternion number h1(θ1(t0),e1(t0)) represents the state. Similarly, we can consider the planet β at time t0 and planets α,β at time t as well. By using the similar analysis of the gyroscope above, the rotation of two planets have an impact on each other, thus we can use dynamic Equation (36) to depict such a rotation which is from the state at time t0 to the state at time t (see Figure 11). Notice that the dynamic Equation (36) can be given as:

hΔ(t)=Φ(t)h(t)

i.e.,

h1Δθ1(t),e1(t)h2Δθ2(t),e2(t)=q11θ11(t),e11(t)q12θ12(t),e12(t)q21θ21(t),e21(t)q22θ22(t),e22(t)h1θ1(t),e1(t)h2θ2(t),e2(t),

with the initial condition

h(t0)=h1(t0)h2(t0)=h1θ1(t0),e1(t0)h2θ2(t0),e2(t0).

Figure 11.

Figure 11

The motion diagram of the planet rotation described by (36) which describe (the state at t0 to the state at t by (36)).

In what follows, a rotation of the planets α,β by a concrete dynamic equation is demonstrated, and the state of the planet at the same time of each day is considered. For this case, the time intervals that we assume are equivalent. Therefore, we consider the dynamic equations on the time scale T=Z as follows (see Example 3).

Example 3

([61]). Letting T=Z, we consider the linear homogenous two-dimensional TQDEs as follows:

hΔ(t)=Φ(t)h(t),Φ(t)=A+Bi+Cj+Dk=Φ11Φ12Φ21Φ22, (41)

with the initial value h(0)=[1,1]T, where

Φ11=15sintsin23.5+15icostsin23.5+15jsintcos23.5+15kcostsin23.5,Φ12=15sin2tsin23.5+15icostsintsin23.5+15jsintcostcos23.5+15kcos2tsin23.5,Φ21=3.8sint+3.8icost+2jsint+2kcost,Φ22=3.8sin2t+3.8icostsint+2jsintcost+2kcos2t.

h(t)=[h11(t)+h12(t)i+h13(t)j+h14(t)k,h21(t)+h22(t)i+h23(t)j+h24(t)k]T=[h^1(t),h^2(t)]. Assume that h(t)=h1+h2i+h3j+h4k, then

hT(t+1)=hT(t)ΦT(t)+hT(t)=h1TATh2TBTh3TCTh4TDT+(h2TAT+h1TBT+h4TCTh3TDT)i+(h3TATh4TBT+h1TCT+h2TDT)j+(h4TAT+h3TBTh2TCT+h1TDT)k+hT(t),

i.e.,

h1T(t+1)=h1TATh2TBTh3TCTh4TDT+h1T,h2T(t+1)=h2TAT+h1TBT+h4TCTh3TDT+h2T,h3T(t+1)=h3TATh4TBT+h1TCT+h2TDT+h3T,h4T(t+1)=h4TAT+h3TBTh2TCT+h1TDT+h4T,

where h(t+1)=h1(t+1)+h2(t+1)i+h3(t+1)j+h4(t+1)k, hwvR, hv,hv(t+1)R2, w,v{1,2,3,4} and A,B,C,DR2×2. Hence, the numerical solution of (41) can be calculated by the following MATLAB code:

clear  
syms h11 h21 h12 h22 h13 h23 h14 h24 t;  
h11=1;h21=1;h12=0;h22=0;h13=0;h23=0;h14=0;h24=0;  
for n=0:1:14;t=n  
h=[h11 h21;h12 h22;h13 h23;h14 h24];  
A=[15*sin(23.5)*sin(t) 15*sin(t)*sin(t)*sin(23.5);  
   3.8*sin(t) 3.8*sin(t)*sin(t)]’;  
B=[15*sin(23.5)*cos(t) 15*cos(t)*sin(t)*sin(23.5);  
  3.8*cos(t) 3.8*cos(t)*sin(t)]’;  
C=[15*cos(23.5)*sin(t) 15*sin(t)*cos(t)*cos(23.5);2*sin(t) 2*sin(t)*cos(t)]’;  
D=[15*sin(23.5)*cos(t) 15*cos(t)*cos(t)*sin(23.5);2*cos(t) 2*cos(t)*cos(t)]’;  
h=1.*[h(1,:)*A-h(2,:)*B-h(3,:)*C-h(4,:)*D;h(2,:)*A + h(1,:)*B + h(4,:)*C-h(3,:)*D;  
    h(3,:)*A-h(4,:)*B + h(1,:)*C + h(2,:)*D;h(4,:)*A + h(3,:)*B-h(2,:)*C + h(1,:)*D] + h  
end  
           

The numerical solution of (41) is demonstrated at Table 2. Next, in real application, we will show the solution h(t) with the planets α,β corresponding state (see Figure 11), without loss of generality, for t=10, we have

h(10)=7.7127+19.2623i0.0340j+1.2122k3.21144.8892i+0.0732j0.1619k=h^1(10)h^2(10)=|h^1(10)|[cosθ1(10)+(i,j,k)e1(10)sinθ1(10)]|h^2(10)|[cosθ2(10)+(i,j,k)e2(10)sinθ2(10)]=|h^1(10)|R(h^1(10))|h^1(10)|+(h^1(10))|(h^1(10))||(h^1(10))||h^1(10)||h^2(10)|R(h^2(10))|h^2(10)|+(h^2(10))|(h^2(10))||(h^2(10))||h^2(10)|=20.784437.712720.78443+19.2623i0.0340j+1.2122k19.3004319.3004320.784425.884313.21145.88431+4.8892i+0.0732j0.1619k4.930724.930725.88431,

i.e., the rotation direction of the planet α in three-dimensional space is e1(10)=(0.99802,0.00176,0.06281) and the rotation angle is θ1(10), where cosθ1(10)=0.37108 and sinθ1(10)=0.9286. Similarly, the rotation direction of the planet β is e2(10)=(0.99158,0.00754,0.03283) and the rotation angle is θ2(10), where cosθ2(10)=0.54575 and sinθ2(10)=0.83794.

Table 2.

The solution of (41).

step t h11(t) h21(t) h12(t) h22(t) h13(t) h23(t) h14(t) h24(t)
0 0 1 1 0 0 0 0 0 0
1 1 1.0000 1.0000 −14.9712 3.8000 0 0 −29.9425 4.0000
2 2 −22.1986 6.8883 −14.8956 3.7808 −1.2036 2.5922 −12.4595 1.6645
3 3 −24.9918 7.5973 11.8954 −3.0193 −0.4930 1.0618 3.6375 −0.4859
4 4 −1.4109 1.6119 16.9130 −4.2929 −0.0013 0.0028 0.1483 −0.0198
5 5 3.7555 0.3006 2.3799 −0.6041 0.2434 −0.5242 3.3894 −0.4528
6 6 1.5897 0.8503 −0.1744 0.0443 1.1430 −2.4619 −5.4514 0.7283
7 7 4.0143 0.2349 -10.3584 2.6292 0.5086 −1.0954 −28.1773 3.7642
8 8 −15.2980 5.1367 −18.7021 4.7470 −1.0700 2.3046 −19.7960 2.6445
9 9 −28.4662 8.4791 4.3334 −1.0999 −0.7850 1.6908 1.8614 −0.2487
10 10 −7.7127 3.2114 19.2623 −4.8892 −0.0340 0.0732 1.2122 −0.1619
11 11 4.7138 0.0574 5.7280 −1.4539 0.0813 −0.1751 2.0216 −0.2701
12 12 1.0001 1.0000 −0.0000 0.0000 0.9327 −2.0088 −0.0666 0.0089
13 13 4.7228 0.0551 −5.8547 1.4860 0.9187 −1.9787 −23.2944 3.1119
14 14 −7.9334 3.2675 −19.2938 4.8972 7 − 0.7442 1.6029 −25.9138 3.4618
15 15 −28.5219 8.4933 −4.0750 1.0343 −1.0456 2.2521 −2.3270 0.3109
step t h1(t) h2(t)
0 0 1 1
1 1 1.0000 − 14.9712i− 29.9425k 1.0000 + 3.8000i + 4.0000k
2 2 −22.1986 − 14.8956i− 1.2036j− 12.4595k 6.8883 + 3.7808i + 2.5922j + 1.6645k
3 3 −24.9918 + 11.8954i− 0.4930j + 3.6375k 7.5973 − 3.0193i + 1.0618j− 0.4859k
4 4 −1.4109 + 16.9130i− 0.0013j + 0.1483k 1.6119 − 4.2929i + 0.0028j− 0.0198k
5 5 3.7555 + 2.3799i + 0.2434j + 3.3894k 0.3006 − 0.6041i− 0.5242j− 0.4528k
6 6 1.5897 − 0.1744i + 1.1430j− 5.4514k 0.8503 + 0.0443i− 2.4619j + 0.7283k
7 7 4.0143 −10.3584i + 0.5086j− 28.1773k 0.2349 + 2.6292i− 1.0954j + 3.7642k
8 8 −15.2980 − 18.7021i− 1.0700j− 19.7960k 5.1367 + 4.7470i + 2.3046j + 2.6445k
9 9 −28.4662 + 4.3334i− 0.7850j + 1.8614k 8.4791 − 1.0999i + 1.6908j− 0.2487k
10 10 −7.7127 + 19.2623i− 0.0340j + 1.2122k 3.2114 − 4.8892i + 0.0732j− 0.1619k
11 11 4.7138 + 5.7280i + 0.0813j + 2.0216k 0.0574 − 1.4539i− 0.1751j− 0.2701k
12 12 1.0001 − 0.0000i + 0.9327j− 0.0666k 1.0000 + 0.0000i− 2.0088j + 0.0089k
13 13 4.7228 − 5.8547i + 0.9187j− 23.2944k 0.0551 + 1.4860i− 1.9787j + 3.1119k
14 14 −7.9334 − 19.2938i− 0.7442j− 25.9138k 3.2675 + 4.8972i + 1.6029j + 3.4618k
15 15 −28.5219 − 4.0750i− 1.0456j− 2.3270k 8.4933 + 1.0343i + 2.2521j + 0.3109k

In the following, a comprehensive application is provided including the rotation theory of quaternions, the Liouville formula, the commutativity of quaternion-matrix-valued functions, the existence and uniqueness of solution for TQDEs, and the quaternion exponential function, and we apply the theory of time scales to show the feasibility of the main results stated in this article.

Example 4

([61]). In this application, we will consider the motion of submarines by the quaternion dynamic equations under time scales background. We use h1(t) to represent the orientations and rotations of α(Roll), h2(t) to represent the orientations and rotations of γ(Yaw) (see Figure 8). Since the submarines have a center of gravity and a center of buoyancy to maintain lateral stability, the function β(Picth) is a constant, which means that we can use (36) to present this submarine’s motion. The initial value h(t0)=[1,k]T represents the initial state of the orientations and rotations of the submarine (see Figure 7). For convenience, the black ring is called roll ring, and the red ring is called yaw ring in Figure 7. Indeed, p11(t) represents the difference value of the roll ring variable. We take p11(t)=t1+2tcosλπ2i+2tsinλπ2k, which implies the roll ring rotates left for λ=0, upward for λ=1. During the voyage of the submarine, the roll ring is affected by the yaw ring. Hence, we take p12(t)=t, i.e., the yaw ring changes the speed of the roll ring instead of its direction. For the yaw ring, it is not subject to the effect of the roll ring. Hence, we take p21(t)=0 and p22(t)=t1+3tk. On the other hand, if QTDE(t)=0, then h1(t) and h2(t) are right dependent, i.e., the roll ring and the yaw ring are in the same plane. Furthermore, for h1(θ1(t),e1(t)) and h2(θ2(t),e2(t)), if e1(t),e2(t) are parallel to each other and they are perpendicular to the horizon simultaneously, then the phenomenon of “Gimbal Lock" happens. For QTDE(t)0, h1(t) and h2(t) are right independent, i.e., the roll ring and the yaw ring are not in the same plane.

As the quaternion dynamic equations are considered on times scales, we shall show the influence of time scales for the motion of submarine as follows. If we steer the submarines from the place A to the place B, there are two routes that can be chosen, i.e., L1 or L2 (see Figure 12). For the route L1, we steer the submarine in an ideal state, i.e., the orientations and rotations of the submarine are continuously changed by considering the corresponding quaternion dynamic equations in T=R case. For the route L2, we steer the submarine from the place A to the place C by the continuous change of the orientations and rotations of the submarine, then steer straight ahead from the place C to the place D, which indicates that the corresponding quaternions value are different at the places A and C, and are equivalent at the places C and D. We denote the interval [t0,t] the time of passing places AB. Obviously, the time that is consumed to change the orientations and rotations of the submarine is a closed subset of [t0,t], i.e., the corresponding quaternion dynamic equations are considered on T[t0,t], which is a time scale. Now, we will calculate the solution, the fundamental matrix, and the Liouville formula for the T=Z case.

Let λ[0,1], T=Z, t0=1, Φ(t)=t1+2ticosλπ2+2tksinλπ2t0t1+3tk, the initial value h(t0)=[1,k]T. Then, the solution of (36) can be given as

h(t)=eΦ(t,1)h(1)=h(1)+n=1+1tΦ(tn)1tnΦ(tn1)1t2Φ(t1)Δt1Δtn1Δtnh(1)=h(1)+n=1t1tΦ(tn)1tnΦ(tn1)1t2Φ(t1)Δt1Δtn1Δtnh(1)={I+Φ(t1)+Φ(t2)++Φ(1)+Φ(t1)[Φ(t2)++Φ(1)]+Φ(t2)[Φ(t3)++Φ(1)]++Φ(t1)Φ(t2)Φ(1)}h(1)=[I+Φ(t1)][I+Φ(t2)][I+Φ(1)]h(1)=(t1)!(1+icosλπ2+ksinλπ2)t1(t1)!l=0t1(1+icosλπ2+ksinλπ2)l[1+3k]t1l0(t1)!(1+3k)t1h(1)=(t1)!(1+icosλπ2+ksinλπ2k)t1+k(t1)!l=0t1(1+icosλπ2+ksinλπ2)l[1+3k]t1l(t1)!(1+3k)t1k.

We say that λ is the steering parameter, i.e., through taking the different values of λ, one can control the submarine’s motion by choosing the corresponding parameter that reflects the different submarine’s states. Assume that

h(t)=h1(t)h2(t)=|h1(t)|R(h1(t))|h1(t)|+(h1(t))|(h1(t))||(h1(t))||h1(t)||h2(t)|R(h2(t))|h2(t)|+(h2(t))|(h2(t))||(h2(t))||h2(t)|=|h1(t)|cosArg(h1(t))+(h1(t))|(h1(t))|sinArg(h1(t))|h2(t)|cosArg(h2(t))+(h2(t))|(h2(t))|sinArg(h2(t))=|h1(t)|cosθ1(t)+e1(t)(i,j,k)sinθ1(t)|h2(t)|cosθ2(t)+e2(t)(i,j,k)sinθ2(t)=h10(t)+h11(t)i+h12(t)j+h13(t)kh20(t)+h21(t)i+h22(t)j+h23(t)k.

For λ[0,1), h1(t) and h2(t) are non-commutative. The roll ring rotates to the left for λ=0, and it rotates to left and upward at the same time for λ(0,1). For λ=1, we have

h(t)=(t1)![1+k]t1+k(t1)!l=0t1(1+k)l[1+3k]t1l(t1)!(1+3k)t1k,

thus

h11(t)h22(t)=h12(t)h21(t)h12(t)h23(t)=h13(t)h22(t)h11(t)h23(t)=h13(t)h21(t),

e1(t),e2(t){(0,0,1),(0,0,0)}. Hence, h1(t),h2(t) are commutative and e1(t),e2(t) are parallel vectors. Moreover, if QTDE(t0)=0, e1(t0)=e2(t0)=(0,0,1), then e1(t),e2(t) are perpendicular to the horizontal plane and the phenomenon of "Gimbal Lock" happens. The fundamental solution matrix can be formulated as

M(t)=(t1)!(1+icosλπ2+ksinλπ2)t1(t1)!l=0t1(1+icosλπ2+ksinλπ2)l[1+3k]t1l0(t1)!(1+3k)t1.

By Theorem 70, we have

τ(t)=p11(t)+p11¯(t)+p22(t)+p22¯(t)+[p11(t)p11¯(t)+p22(t)p22¯(t)+(p11(t)+p11¯(t))(p22(t)+p22¯(t))(p12(t)p21(t)+p21¯(t)p12¯(t))]μ(t)+[p11(t)p11¯(t)(p22(t)+p22¯(t))+(p11(t)+p11¯(t))p22(t)p22¯(t)(p11(t)p21¯(t)p12¯(t)+p12(t)p21(t)p11¯(t))(p12(t)p22¯(t)p21(t)+p21¯(t)p22(t)p12¯(t))]μ2(t)+[p11(t)p11¯(t)p22(t)p22¯(t)+p12(t)p12¯(t)p21(t)p21¯(t)p12(t)p22¯(t)p21(t)p11¯(t)p11(t)p21¯(t)p22(t)p12¯(t)]μ3(t)=p11(t)+p11¯(t)+p22(t)+p22¯(t)+p11(t)p11¯(t)+p22(t)p22¯(t)+(p11(t)+p11¯(t))(p22(t)+p22¯(t))+p11(t)p11¯(t)(p22(t)+p22¯(t))+(p11(t)+p11¯(t))p22(t)p22¯(t)+p11(t)p11¯p22(t)p22¯(t)=2t2+2t2+(t1)2+4t2+(t1)2+9t2+4(t1)2+[(t1)2+4t2](2t2)+[(t1)2+9t2](2t2)+[(t1)2+4t2][(t1)2+9t2]=15t41.

Hence, the Wronskian of TQDEs with QTDE(t0)=1 can be calculated as:

QTDE(t)=eτ(t,1)QTDE(1)=1+n=1+1tτ(tn)1tnτ(tn1)1t2τ(t1)Δt1Δtn1Δtn=1+n=1t1tτ(tn)1tnτ(tn1)1t2τ(t1)Δt1Δtn1Δtn=1+τ(t1)+τ(t2)++τ(1)+τ(t1)[τ(t2)++τ(1)]+τ(t2)[τ(t3)++τ(1)]++τ(t1)τ(t2)τ(1)=[1+τ(t1)][1+τ(t2)][1+τ(1)]=[15×(t1)!]t1.

On the other hand, τ(t)=4t4 and QTDE(t)=e2t24t6QTDE(1) for T=R.

Figure 12.

Figure 12

The motion diagram of submarine.

5. The Coupled-Jumping Theory on Time Scales

In 2020, Wang, Li, Agarwal, and O’Regan proposed the coupled-jumping theory. It is an interesting topic and can include the Hilger theory and can be used to solve the problems on more general hybrid time scales (see [62,63]).

5.1. Vertical Evolution of Time Scales

In Figure 13, let {T1,T2,T3,T4} be a timescale group. By Hilger theory, this time scale group will induce a continuous dynamic equation, a piecewise continuous dynamic equation, a discrete dynamic equation, and a quantum dynamic equation in sequence. Starting with the evolution process of these time scales, T varies from the form T1 to the form T4 in the timescale group, such a vertical evolution in the timescale group acts as a direct factor which leads to the four different types of dynamic equations during the changing process of the time scale T. Only when T is fixed in this timescale group can the concrete dynamic equation be determined. From the viewpoint of the evolution process of time scales, the essence of Hilger’s theory depends on the vertical evolution of time scales; accordingly, the unification of various types of dynamic equation can be achieved when the form of T is fixed in a timescale group. In other words, the related analysis and applications on Hilger theory are purely based on a single time scale during this evolution.

Figure 13.

Figure 13

The vertical evolution diagram of dynamical behavior from T1 to T4 under Hilger theory.

5.2. Hybrid-Timescale Problems—A Horizontal Evolution of Time Scales

The other natural and significant evolution of time scales that must be referred to is horizontal evolution of time scales. The related problems caused by horizontal evolution of time scales cannot be solved by Hilger theory and they still belong to the problems of timescale category. In Figure 14, let

T1=qn:q>1,nZ{0}¯,T2=[1.1,3.7],T3=k=25[2k,2k+1],
T4={12.1,13.1,14.1,15.1,16.1},T5={(1.5)n:n7}¯,.

For convenience, let a timescale group be formed by {T1,T2,T3,T4,T5,}. It is easy to observe that the dynamical behavior described by Figure 14 exists on the time scale T formed by five districts, and each district is a time scale, i.e., T=T1T2T3T4T5. Therefore, the switch of the dynamical behavior in four timescale districts is directly caused by a horizontal evolution of all the time scales in this timescale group.

Figure 14.

Figure 14

The horizontal evolution diagram of dynamical behavior from T1 to T4 under coupled-jumping timescale theory.

Usually, all the similar problems described by Figure 14 are called the hybrid-timescale problems. Essentially, the hybrid-timescale problems are formed by the problems on multiple time scales, and this class of problems can be precisely depicted by a horizontal evolution of time scales in a timescale group.

By comparison, the related hybrid-timescale problems are more comprehensive and will strictly include the problems on a single time scale as their particular cases (see Figure 15 for their detailed relations). Moreover, the dynamical behavior on hybrid time scales cannot be effectively studied purely on a single time scale through Hilger theory. Therefore, it is very necessary to establish a theory (we call it coupled-jumping timescale theory) to solve the hybrid-timescale problems.

Figure 15.

Figure 15

The relation among hybrid-timescale problems, single-timescale problems, Hilger theory and coupled-jumping timescale theory.

5.3. The Description of the Hybrid-Timescale Initial-Value Problems

For understanding the idea to solve the hybrid-timescale problems, we will adopt Figure 14 to illustrate our methods and the framework of the solving steps. Let a timescale group be {T1,T2,T3,T4,T5,}. To break through the limitation of the Hilger theory and to establish a coupled-jumping timescale theory, demonstrating a distinct dynamical behavior on time scales, firstly, we must consider the formation process of the dynamical behavior in Figure 14. Assume that the dynamical behavior in Figure 14 corresponds to a solution x(t) of a dynamic equation on the hybrid time scales with the initial point (t0,x(t0)), where t0=0T1. According to the continuous dependence on initial values of solutions and the continuation theorem, there is a solution on the district T1 such that (t1,x(t1)) is the right boundary point on the district T1, where t1=1T2. Now taking (t1,x(t1)) as the initial point, there is a solution on the district T2 such that (t2,x(t2)) is the right boundary point on the district T2, where t2=3.7T3. Next, by taking (t2,x(t2)) as the initial point, there is a solution on the district T3 such that (t3,x(t3)) is the right boundary point on the district T3, where t3=11T4. Repeating the process, by taking (t3,x(t3)) as the initial point, there is a solution on the district T4 such that (t4,x(t4)) is the right boundary point on the district T4, where t4=16.1T5. Finally, the solution on the district T5 is determined by the initial point (t4,x(t4)). If there are more time scales after T5, for instance, T6,T7,, the process above can be continued until the solution exists on T1T2T3:=i=1+Ti.

In the above process, a key problem appears. Note that t1T2, but the solution on district T2 is continuously dependent on (t1,x(t1)); similarly, t2T3, but the solution on district T3 is continuously dependent on (t2,x(t2)),…, t4T5, but the solution on district T5 is continuously dependent on (t4,x(t4)),. Therefore, the first problem we must solve is that we should introduce an initial value problem of a dynamic equations whose initial value is given in one time scale and the unique solution is located in another. In Ref. [62], the coupled-jumping timescale theory (or hybrid-timescale theory) was proposed.

5.4. The Coupled-Jumping Timescale Space (CJTS) and Calculus

A notion of coupled-jumping timescale space and a concept of the hybrid-composition integral was introduced.

Definition 71

([62]). For t^Tk, we define the forward jump operator σk:TkTk by σk(t^)=inf{sTk:s>t^}; the backward jump operator ρk:TkTk by ρk(t^)=sup{sTk:s<t^}; and the graininess function μk:Tk[0,+) by μk(t^)=σk(t^)t^, where k=1,2.

The jumping construction of the coupled-jumping timescale space T1T2 was defined.

Definition 72

([62]). Let T1 and T2 be a pair of time scales. For tT1T2, we define the coupled-forward jump operator between T1 and T2 by σT2(t)=inf{sT2:st}, and define the coupled-backward jump operator between T1 and T2 by ρT2(t)=sup{sT2:st}. We say t is a coupled right-dense point iff σT2(t)=t; t is a coupled right-scattered point iff σT2(t)>t; t is a coupled left-dense point iff ρT2(t)=t; t is a coupled left-scattered point iff ρT2(t)<t; t is a coupled isolated point iff ρT2(t)<t<σT2(t) (see Figure 16).

Figure 16.

Figure 16

Schematic diagram of all types of coupled-jumping points.

Remark 10.

In Definition 72, one can obtain σT2(t2)=ρT2(t2)=t2 for t2T2; ρT1σT2(t)t for tT1; σT2ρT1(t2)t2 for t2T2. Note that ρT1σT2(t)=t if and only if t,σT2(t)T1=; σT2ρT1(t2)=t2 if and only if ρT1(t2),t2T2=, where is an empty set (see Figure 17).

Figure 17.

Figure 17

The jump of coupled-jumping points in Remark 10.

Definition 73

([62]). Let T1 and T2 be a pair of time scales. We define Tkκ´ and Tkκ as follows:

Tkκ´=Tk(supTj,+)ifsupTj is a finite number,Tkotherwise,Tkκ=Tk(,infTj)ifinfTj is a finite number,Tkotherwise,

where k,j{1,2} and kj.

Definition 74

([62]). Let T1 and T2 be a pair of time scales. We define Tkκ¯ as follows:

Tkκ¯=Tk(,infTj)(supTj,+)ifinfTj,supTj are finite numbers,Tk(,infTj)ifinfTj is a finite number, supTj=+,Tk(supTj,+)ifsupTj is a finite number, infTj=,Tkotherwise,

where k,j{1,2} and kj.

Remark 11.

In Definition 74, if T1=T2=T, then Tκ¯=T and a Hilger time scale is obtained.

Remark 12.

In Definitions 73 and 74, we obtain that Tkκ¯=Tkκ´Tkκ.

Remark 13.

Note that a,bT1T2 and [a,b]Tj, for a<b and j=1,2, one can obtain [a,b]T1=σT1(a),ρT1(b)T1 and [a,b]T2=σT2(a),ρT2(b)T2. Let a˜=maxσT1(a),σT2(a) and b˜=minρT1(b),ρT2(b). Then, σTj(a),ρTj(b)Tjκ´=[σTj(a),ρTj(b˜)]Tj, σTj(a),ρTj(b)Tjκ=σTj(a˜),ρTj(b)Tj, σTj(a),ρTj(b)Tjκ¯=σTj(a˜),ρTj(b˜)Tj, where j{1,2} (see Figure 18). Notice that, for any a^,b^Tj, the intervals [a^,b^)Tj,(a^,b^)Tj with a^b^ are always regarded as the empty sets. According to the Δ-measure theory on time scales [25], it is well-known that the Δ-integral of a function f(t) equals to zero on the empty set since μΔ()=0.

Figure 18.

Figure 18

The jump of coupled-jumping points in Remark 13.

Theorem 74

([62]). Let t1T1κ. If ρT2σ1(t1)=σ2ρT2(t1) and μ1(t1)=μ2ρT2(t1), then ρT2(t1)t1ρT2σ1(t1)σ1(t1).

Remark 14.

In Theorem 74, if t1T1T2, then ρT2(t1)=t1 and ρT2σ1(t1)=σ1(t1).

Theorem 75

([62]). Assume ρT2σ1(t1)=σ2ρT2(t1) and μ1(t1)=μ2ρT2(t1) for any t1T1κ. Then, ρT2σT1(t2)=t2 for any t2T2κ´ (see Figure 19).

Figure 19.

Figure 19

The jump situation for the coupled-jumping points in Theorem 75.

Definition 75

([62]). Let f:T1T2R. We define a hybrid-composition integral (or short for HC-integral) of f(t) on CJTS as follows:

abf(τ)Δmτ=α[σT1(a),ρT1(b)]T1f(τ)Δ1τ+(1α)[σT2(a),ρT2(b)]T2f(τ)Δ2τ,a<b,α[σT1(b),ρT1(a)]T1f(τ)Δ1τ(1α)[σT2(b),ρT2(a)]T2f(τ)Δ2τ,a>b,

where a,bT1T2, 0α1 and α is called the hybrid-composition proportion coefficient.

Theorem 76

([62]). If a,b,cT1T2, α˜R, f,g:T1T2R, then

  • (i)

    Let [ak,ak+1]Tl, k,l{1,2} and {a,b,c}={aj|j=1,2,3,a1<a2<a3}. Then, abf(τ)Δmτ=acf(τ)Δmτ+cbf(τ)Δmτ if a2T1T2; abf(τ)Δmτacf(τ)Δmτ+cbf(τ)Δmτ if a2T1T2;

  • (ii)

    abf(τ)+g(τ)Δmτ=abf(τ)Δmτ+abg(τ)Δmτ;

  • (iii)

    abα˜f(τ)Δmτ=α˜abf(τ)Δmτ;

  • (iv)

    abf(τ)Δmτ=baf(τ)Δmτ;

  • (v)

    aaf(τ)Δmτ=0;

  • (vi)

    abf(τ)Δmτ0 if f0 for all aτ<b.

In the following, we introduce the exponential function on coupled-jumping time scales and describe the basic theory of time-hybrid dynamic equations.

Definition 76

([62]). Let tˇ,sT1T2. We introduce the HC-exponential function by

e¯f(tˇ,s):=expα[σT1(s),ρT1(tˇ)]T1Log(1+μ1(τ)f(τ))μ1(τ)Δ1τ+(1α)[σT2(s),ρT2(tˇ)]T2Log(1+μ2(τ)f(τ))μ2(τ)Δ2τs<tˇ,expα[σT1(tˇ),ρT1(s)]T1Log(1+μ1(τ)f(τ))μ1(τ)Δ1τ(1α)[σT2(tˇ),ρT2(s)]T2Log(1+μ2(τ)f(τ))μ2(τ)Δ2τs>tˇ.

Next, we demonstrate the HC-exponential solution of the homogeneous time-hybrid dynamic equation.

Theorem 77

([62]). Let tT1κ¯, sT2κ¯, ts. Then, e¯f(t,s) is the solution of the initial value problem

μ1(t)xΔt(t)=1+μ1(t)f(t)αexp(1α)ρT2(t)ρT2(σ1(t))Log(1+μ2(τ)f(τ))μ2(τ)Δ2τ1x(t), (42)

with the initial value x(s)=1, where xΔt(t) denotes the Δ-derivative at t on T1.

The theorem below is the existence and uniqueness theorem of the HC-exponential solution to the homogeneous time-hybrid dynamic equation on CJTS.

Theorem 78

(Existence and Uniqueness of Solutions, [62]). For the initial value problem of (42), there exists a unique solution x(t)=x0e¯f(t,s).

Based on the theory, the time-hybrid dynamic equations, convolution, and Laplace transforms were proposed and studied in [62] in detail.

6. Combined Measure Theory on Time Scales

The measure theory on time scales was considered in [64,65]. The combined theory on time scales was initiated in [66], and it was widely used in mathematical analysis. In [67], the authors obtained the non-eigenvalue form of Liouville’s formula and α-matrix exponential solutions for combined matrix dynamic equations on time scales. In 2020, Wang, Qin, Agarwal, and O’Regan (see [68]) established the α-measurability and combined measure theory on time scales.

6.1. α-Measurability and α-Measure

Definition 77

([68]). Let T be a time scale, σ and ρ be the forward and back jumping operators, and a combined interval (or α-interval) be

[a,b]α:=(a,b]T,α=0,(a,b)T,0<α<1,[a,b)T,α=1,

where (a,b]T={tT:a<tb,a,bT}, (a,b)T={tT:a<t<b,a,bT}, [a,b)T={tT:at<b,a,bT}. Let K be the family of all combined intervals.

Then, we present the set function mα corresponding to [a,b]α as

mα([a,b]α)=ba,α=0,α(bσ(a))+(1α)(ρ(b)a),α(0,1),ba,α=1.

For a=b, we appoint that [a,b]α=, and mα([a,b]α)=0.

Definition 78

([68]). Let ET. If there exists at least one finite or countable system of intervals [an,bn]αK(n=1,2,...) such that EnN0[an,bn]α, then we call mα(E)=infnN0mα([an,bn]α) the outer α-measure of E, where the infimum is taken over all coverings of E by a finite or countable system of intervals [an,bn]αK. If there is no such covering of E, we say mα(E)=.

Definition 79

([68]). We say a property that holds everywhere except for a null set is α-almost everywhere, briefly α-a.e. in combined measure theory on time scales.

Theorem 79

([68]). Let AT, BT and mα(A), mα(B) be the outer α-measure of A and B, respectively. Then,

  • (1)

    mα(A)0, if A=, then mα(E)=0;

  • (2)

    let AB, then mα(A)mα(B);

  • (3)

    mα(i=1Ai)i=1mα(Ai).

Definition 80

([68]). A set ET is called α-measurable (or mα-measurable) if

mα(Pα)=mα(PαE)+mα(PαEc)

holds for all PαK, where Ec=TE. We let N(mα) be the family of all mα-measurable sets as

N(mα)={ET:Eismα-measurable}.

The following sufficient and necessary condition for α-measurability can be established.

Theorem 80

([68]). Letting ET is α-measurable if and only if for any AE,BEc, we have

mα(AB)=mα(A)+mα(B).

Theorem 81

([68]). Let {Ei} be a sequence pairwise disjoint α-measurable sets, then i=1Ei is α-measurable, and

mα(i=1Ei)=i=1mα(Ei).

Now, the Lebesgue α-measure denoted by μα is mα restricted to N(mα), and it is a countably additive measure.

Theorem 82

([68]). Let {Ei} be an increasing sequence of α-measurable set in T, such that E1E2En, then let E=i=1Ei=limnEn, and we have

μα(E)=limnμα(En).

If {En} is a decreasing sequence of α-measurable set in T such that E1E2En, let E=i=1Ei=limnEn, then, when μα(E1)<, we have

μα(E)=limnμα(En).

Some basic theorems and lemmas were obtained.

Theorem 83

([68]). If a,bT{minT,maxT} and a<b, then

  • (i)

    μα((a,b))=α(bσ(a))+(1α)(ρ(b)a).

  • (ii)

    μα((a,b])=α(σ(b)σ(a))+(1α)(ba).

  • (iii)

    μα([a,b))=α(ba)+(1α)(ρ(b)ρ(a)).

  • (iv)

    μα([a,b])=α(σ(b)a)+(1α)(bρ(a)).

Remark 15.

Notice that μα=μ when α=0, and μα=μΔ when α=1, and if α(0,1), μα is a linear combination of μ and μΔ. Thus, for any interval ET, we can conclude as follows:

μα(E)=αμΔ(E)+(1α)μ(E),α[0,1].

6.2. Lebesgue Measurable and Lebesgue α-Measurable Sets

In this subsection, we denote the usual Lebesgue measure on R by L and the corresponding outer measure by L, i.e.,

L(E)=infjJ(βjαj):EjJ(αj,βj),αj,βjR,αjβj,JN0.

From the above, we can easily see that the set of all left-scattered points of T is also countable; then, the set of all isolate points is countable. For the convenience, we define the following sets:

A:={tT:tis left-dense and right-scattered},B:={tT:tis left-scattered and right-dense},C:={tT:tis left-scattered and right-scattered},D:={tT:tis left-dense and right-dense}. (43)

Theorem 84

([68]). If ET{maxT,minT}, then the following properties are satisfied:

  • (a)

    L(E)mα(E).

  • (b)

    If E has no scattered points, then L(E)=mα(E).

  • (c)
    The sets A,B,C,D defined in (43) are Lebesgue measurable. Moreover L(A)=L(B)=L(C)=0. In addition,
    μα(EA)=αiIEA(σ(ti)ti),μα(EB)=(1α)iIEB(tiρ(ti)),μα(EC)=αiIEC(σ(ti)ti)+(1α)iIEC(tiρ(ti)),
    where IEA,IEB,IEC indicates the indices set for all right-scattered and left-dense points, the indices set for all left-scattered and right-dense points, and the indices set for all left-scattered and right-scattered points in E, respectively.
  • (d)

    mα(E)=L(E)+αiIE(AC)(σ(ti)ti)+(1α)iIE(BC)(tiρ(ti)).

  • (e)

    mα(E)=μL(E) if and only if E has no scattered points.

Theorem 85

([68]). Let ET, then E is Lebesgue α-measurable if and only if it is Lebesgue measurable. In such a case, for ET{maxT,minT}, the following is true:

  • (i)

    μα(E)=L(E)+αiIESR(σ(ti)ti)+(1α)iIESL(tiρ(ti)), where IESR and IESL denote the index set of all right-scattered points of E and the index set of all left-scattered points of E, respectively.

  • (ii)

    L(E)=μα(E) if and only if maxTE,minTE and E has no scattered points.

Remark 16.

Using Theorem 85, we get

μα(E)=αL(E1)+(1α)L(E2),

where ET{minT,maxT}, α[0,1] and E1,E2 are the extension of E. In fact, through direct calculation, we have

μα(E)=αiIE(AC)(σ(ti)ti)+(1α)iIE(AC)(tiρ(ti))+L(E)=αiIE(AC)L((ti,σ(ti)))+(1α)iIE(AC)L((ρ(ti),ti))+αL(E)+(1α)L(E)=α(L(iIE(ti,σ(ti)))+L(E))+(1α)(L(jJE(ρ(tj),tj)))+L(E)=αL(iIE(ti,σ(ti))E)+(1α)L(jJE(ρ(ti),tj)(E))=αL(E1)+(1α)L(E2).

Theorem 86

([68]). Let ET, then E is Lebesgue α-measurable if and only if it is Lebesgue measurable. In such a case, for ET{maxT,minT}, the following is true:

  • (i)

    μα(E)=L(E)+αiIESR(σ(ti)ti)+(1α)iIESL(tiρ(ti)), where IESR and IESL denote the index set of all right-scattered points of E and the index set of all left-scattered points of E, respectively.

  • (ii)

    L(E)=μα(E) if and only if maxTE,minTE and E has no scattered points.

6.3. Lebesgue–Stieltjes α-measurability

Definition 81

([69]). The function mαβ:JT[0,+) is called a pre-measure if the following equalities are satisfied:

  • (i)

    mαβ[a,b)=αβ(b)β(a)+(1α)β(ρ(b))β(ρ(a)),

  • (ii)

    mαβ[a,b]=αβ(σ(b)+)β(a)+(1α)β(b+)β(ρ(a)),

  • (iii)

    mαβ(a,b]=αβ(σ(b)+)β(σ(a)+)+(1α)β(b+)β(a+),

  • (iv)

    If b>σ(a), mαβ(a,b)=αβ(b)β(σ(a)+)+(1α)β(ρ(b))β(a+),

where α[0,1], JT denotes the family of all intervals of T, β:TR is a monotone increasing function.

Then, the notion of Lebesgue–Stieltjes α-outer measure mαβ was introduced as follows.

Definition 82

([69]). The function mαβ:JT[0,+) associated with β defined by

mαβ(E)=infi=1mαβ(In),

is called a Lebesgue–Stieltjes α-outer measure of E if there exists at least one finite or countable covering system of intervals InJT of E satisfies En=1In. We say mαβ(E)= if there is no such a covering of E. If

mαβ(A)=mαβ(AE)+mαβ(AEc)

holds for all AT, then we say E is mαβ-measurable (or βα-measurable).

In the following, the symbol M(mαβ) denotes the family of all mαβ-measurable subsets of T, then it forms a σ-algebra. We will use the symbols μΔβ, μβ to denote the Lebesgue–Stieltjes Δ-measure and the Lebesgue–Stieltjes ∇-measure, respectively.

Definition 83

([69]). The function mαβ:JT[0,+) restricted to M(mαβ) is called a Lebesgue–Stieltjes α-measure and denoted by μαβ.

We know that each interval on T can be covered by itself, which is the smallest cover, i.e., any interval is βα-measurable, thus for any interval I, pre-measure mαβ(I) and βα-measure μαβ(I) coincide, i.e.,

  • (i)

    μαβ[a,b)=αβ(b)β(a)+(1α)β(ρ(b))β(ρ(a)),

  • (ii)

    μαβ[a,b]=αβ(σ(b)+)β(a)+(1α)β(b+)β(ρ(a)),

  • (iii)

    μαβ(a,b]=α(β(σ(b)+)β(σ(a)+))+(1α)(β(b+)β(a+)),

  • (iv)

    If b>σ(a), μαβ(a,b)=αβ(b)β(σ(a)+)+(1α)β(ρ(b))β(a+).

Remark 17.

Note that the μαβ measure value of a set ET is the following combination

μαβE=αμΔβ(E)+(1α)μβ(E),

and we can obtain the μΔβ measure if α=1 and the μβ measure if α=0.

Theorem 87

([69]). Let {c}T. Then, it is μαβ-measurable and

μαβ{c}=μαβ[c,c]=αβ(σ(c)+)β(c)+(1α)β(c+)β(ρ(c)).

Remark 18.

There is a fact that [c,c], (ρ(c),c] and [c,σ(c)) all have the same α-measure, but their μαβ-measures are completely different. For μαβ-measure, we need to consider one-sided limits of a monotone increasing function β at the endpoints of a given interval.

Example 5

([69]). Let T=[0,3]{7}[8,9], and

β(t)=x+1if0t3,5if3<t<8,x2if8t9.

Now, we calculate μα-measure and μαβ-measure of the following sets:

ρ(7),7,[7,7],7,σ(7).
  • (1) 

    Consider the μα-measure of the above sets:

  • 1.

    μα(ρ(7),7]=ασ(7)7+(1α)(7ρ(7))=43α.

  • 2.

    μα[7,7]=ασ(7)7+(1α)(7ρ(7))=43α.

  • 3.

    μα[7,σ(7))=ασ(7)7+(1α)(7ρ(7))=43α.

  • (2) 

    Consider the μαβ-measure of the above sets:

  • 1.

    μαβ(ρ(7),7]=αβ(σ(7)+)β(7+)+(1α)7+β(ρ(7)+)=59α.

  • 2.

    μαβ[7,7]=αβ(σ(7)+)β(7)+(1α)7+β(ρ(7))=58α+1.

  • 3.

    μαβ[7,σ(7))=αβ(σ(7))β(7)+(1α)7β(ρ(7))=1α.

Example 6

([69]). (1) Let T=R, then μαβ and μβ measures coincide since for all tT,σ(t)=ρ(t)=t.

  • (2) 

    Let T=Z, then

  • 1.

    μαβ[a,b)=αβ(b)β(a)+(1α)β(b1)β(a1),

  • 2.

    μαβ[a,b]=αβ(b+1)β(a)+(1α)β(b)β(a1),

  • 3.

    μαβ(a,b]=αβ(b+1)β(a+1)+(1α)β(b)β(a),

  • 4.

    For b>a+1, μαβ(a,b)=αβ(b)β(a+1)+(1α)β(b1)β(a).

  • (3) 

    Let β:TT and β(t)=t, then μαβ-measure turns into α-measure as follows:

  • 1.

    μαβ[a,b)=α(ba)+(1α)ρ(b)ρ(a),

  • 2.

    μαβ[a,b]=ασ(b)a+(1α)bρ(a),

  • 3.

    μαβ(a,b]=ασ(b)σ(a)+(1α)(ba),

  • 4.

    If b>σ(a), μαβ(a,b)=αbσ(a)+(1α)ρ(b)a,

which is equivalent to Theorem 80.

Example 7

([69]). Let T=[0,4]{5}[7,10] and

β(t)=1+etif0t2,5if2<t<4,3t4if4t<8,2t2+3if8t10.

Now, we calculate μΔβ-measure, μβ-measure and μαβ-measure of the following sets:

{3},{4},[4,7),(8,9],{5},[0,1).

(1) Consider the μΔβ-measures of the above sets:

  • 1.

    μΔβ{3}=βσ(3)+β3=β(3)β(3)=0,

  • 2.

    μΔβ{4}=β(σ(4)+)β(4)=β(5+)β(4)=115=6,

  • 3.

    μΔβ[4,7)=β(7)β(4)=175=12,

  • 4.

    μΔβ(8,9]=β(σ(9)+)β(σ(8)+)=β(9)β(8)=34,

  • 5.

    μΔβ{5}=β(σ(5)+)β(5)=β(7)β(5)=6,

  • 6.

    μΔβ[0,1)=β(1)β(0)=, since the limit from the left-hand side of β at t=0 is not defined.

(2) Consider the μβ-measure of above sets:

  • 1.

    μβ{3}=β(3+)β(ρ(3))=β(3)β(3)=0,

  • 2.

    μβ{4}=β(4+)βρ(4)=85=3,

  • 3.

    μβ[4,7)=βρ(7)βρ(4)=115=6,

  • 4.

    μβ(8,9]=β(9+)β(8+)=β(9)β(8)=34,

  • 5.

    μβ{5}=β(5+)β(ρ(5))=115=6,

  • 6.

    μβ[0,1)=βρ(1)βρ(0)= since the limit from the left-hand side of β at t=0 is not defined.

(3) Consider the μαβ-measure of the above sets:

  • 1.

    μαβ{3}=μαβ[3,3]=α(β(σ(3)+)β(3))+(1α)(β(3+)β(ρ(3)))=α(β(3+)β(3))+(1α)(β(3+)β(3))=0,

  • 2.

    μαβ{4}=μαβ[4,4]=α(β(σ(4)+)β(4))+(1α)(β(4+)β(ρ(4)))=α(β(5+)β(4))+(1α)(β(4+)β(4))=6α+3(1α)=3+3α,

  • 3.

    μαβ[4,7)=αβ(7)β(4)+(1α)(βρ(7)βρ(4))=αβ(7)β(4)+(1α)β(5)β(4)=12α+6(1α)=6+6α,

  • 4.

    μαβ(8,9]=αβ(σ(9)+βσ(8)+)+(1α)β(9+)β(8+)=αβ(9+)β(8+)+(1α)β(9+)β(8+)=34,

  • 5.

    μαβ{5}=μαβ([5,5])=α(β(σ(5)+)β(5))+(1α)(β(5+)β(ρ(5)))=6,

  • 6.

    μαβ[0,1)=αβ(1)β(0)+(1α)β(ρ(1))β(ρ(0))= since the limit from left-hand side of β at t=0 and β(ρ(0)) are not defined.

Remark 19.

Through Example 7, one can obtain that the μαβ measure value of a set ET is the following combination

μαβE=αμΔβ(E)+(1α)μβ(E),

and we can obtain the μΔβ measure if α=1 and the μβ measure if α=0.

7. Conclusions

In this review article, we present a survey of abstract analysis and applied dynamic equations on hybrid time scales. The content is divided into five sections including the almost periodic and almost automorphic theory, the uncertainty theory, the quaternion theory, coupled-jumpping theory, and combined measure theory on hybrid time scales. In each section, we demonstrate the very recent new results on both pure and applied mathematics, which is mainly in function analysis and applied dynamic equations. Moreover, the framework of knowledge and the idea of each section is clearly presented, and the potential future work is illustrated. The results presented in this article can be extended and generalized to study both pure mathematical analysis and real applications such as mathematical physics, biological dynamical models, and neural networks, etc.

Author Contributions

The authors contributed equally to the manuscript and approved this final version. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by NSFC (No. 11961077).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data sharing not applicable.

Conflicts of Interest

The authors declare that they have no competing interests.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Hilger S. Ph.D. Thesis. Universität Würzburg; Wurzburg, Germany: 1988. Ein Maßkettenkalkül mit Anwendung auf Zentrumsmannigfaltigkeiten. [Google Scholar]
  • 2.Bohner M., Peterson A. Dynamic Equations on Time Scales An Introduction with Applications. Birkhäuser Boston; Boston, MA, USA: 2001. [Google Scholar]
  • 3.Bohner M., Peterson A. Advances in Dynamic Equations on Time Scales. Birkhäuser; Boston, MA, USA: 2003. [Google Scholar]
  • 4.Besicovitch A.S. Almost Periodic Functions. Cambridge University Press; Cambridge, UK: 1932. [Google Scholar]
  • 5.Bochner S. Beitrage zur Theorie der fastperiodischen Funktionen. Math. Annalen. 1926;96:119–147. doi: 10.1007/BF01209156. [DOI] [Google Scholar]
  • 6.Bohr H. Zur Theorie der fastperiodischen Funktionen I. Acta Math. 1925;45:29–127. doi: 10.1007/BF02395468. [DOI] [Google Scholar]
  • 7.Veech W.A. Almost automorphic functions. Proc. Natl. Acad. Sci. USA. 1963;49:462–464. doi: 10.1073/pnas.49.4.462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Veech W.A. Almost Automorphic Functions on Groups. Am. J. Math. 1965;87:719–751. doi: 10.2307/2373071. [DOI] [Google Scholar]
  • 9.N’Guérékata G.M. Almost Automorphic Functions and Almost Periodic Functions in Abstract Spaces. Kluwer; New York, NY, USA: 2001. [Google Scholar]
  • 10.N’Guérékata G.M. Topics in Almost Automorphy. Springer; New York, NY, USA: 2005. [Google Scholar]
  • 11.Agarwal R.P., Wang C., O’Regan D. Recent development of time scales and related topics on dynamic equations. Mem. Differ. Equ. Math. Phys. 2016;67:131–135. [Google Scholar]
  • 12.Wang C., Agarwal R.P. Relatively dense sets, corrected uniformly almost periodic functions on time scales, and generalizations. Adv. Differ. Equ. 2015;312:1–9. doi: 10.1186/s13662-015-0650-0. [DOI] [Google Scholar]
  • 13.Wang C., Agarwal R.P., O’Regan D. Periodicity, almost periodicity for time scales and related functions. Nonauton. Dyn. Syst. 2016;3:24–41. doi: 10.1515/msds-2016-0003. [DOI] [Google Scholar]
  • 14.Agarwal R.P., O’Regan D. Some comments and notes on almost periodic functions and changing-periodic time scales. Electr. J. Math. Anal. Appl. 2018;6:125–136. [Google Scholar]
  • 15.Wang C. Existence and exponential stability of piecewise mean-square almost periodic solutions for impulsive stochastic Nicholson’s blowflies model on time scales. Appl. Math. Comput. 2014;248:101–112. doi: 10.1016/j.amc.2014.09.046. [DOI] [Google Scholar]
  • 16.Wang C., Agarwal R.P. Uniformly rd-piecewise almost periodic functions with applications to the analysis of impulsive Δ-dynamic system on time scales. Appl. Math. Comput. 2015;259:271–292. [Google Scholar]
  • 17.Wang C. Piecewise pseudo almost periodic solution for impulsive non-autonomous highorder Hopfield neural networks with variable delays. Neurocomputing. 2016;171:1291–1301. doi: 10.1016/j.neucom.2015.07.054. [DOI] [Google Scholar]
  • 18.Wang C. Almost periodic solutions of impulsive BAM neural networks with variable delays on time scales. Commun. Nonlinear Sci. Numer. Simul. 2014;19:2828–2842. doi: 10.1016/j.cnsns.2013.12.038. [DOI] [Google Scholar]
  • 19.Wang C., Agarwal R.P. Exponential dichotomies of impulsive dynamic systems with applications on time scales. Math. Meth. Appl. Sci. 2015;38:3879–3900. doi: 10.1002/mma.3325. [DOI] [Google Scholar]
  • 20.Wang C., Agarwal R.P. Weighted piecewise pseudo almost automorphic functions with applications to abstract impulsive ∇-dynamic equations on time scales. Adv. Differ. Equ. 2014;153:1–29. doi: 10.1186/1687-1847-2014-153. [DOI] [Google Scholar]
  • 21.Wang C., Agarwal R.P., O’Regan D. Π-semigroup for invariant under translations time scales and abstract weighted pseudo almost periodic functions with applications. Dyn. Syst. Appl. 2016;25:1–28. [Google Scholar]
  • 22.Wang C., Agarwal R.P. Almost automorphic functions on semigroups induced by complete-closed time scales and application to dynamic equations. Discret. Contin. Dyn. Syst. B. 2020;25:781–798. doi: 10.3934/dcdsb.2019267. [DOI] [Google Scholar]
  • 23.Wang C., Agarwal R.P. A classification of time scales and analysis of the general delays on time scales with applications. Math. Meth. Appl. Sci. 2016;39:1568–1590. doi: 10.1002/mma.3590. [DOI] [Google Scholar]
  • 24.Wang C., Agarwal R.P. A Further study of almost periodic time scales with some notes and applications. Abstr. Appl. Anal. 2014;2014:267384. doi: 10.1155/2014/267384. [DOI] [Google Scholar]
  • 25.Wang C., Agarwal R.P., O’Regan D., Sakthivel R. A computation method of Hausdorff distance for translation time scales. Appl. Anal. 2020;99:1218–1247. doi: 10.1080/00036811.2018.1529303. [DOI] [Google Scholar]
  • 26.Wang C., Agarwal R.P., O’Regan D. Weighted piecewise pseudo double-almost periodic solution for impulsive evolution equations. J. Nonlinear Sci. Appl. 2017;10:3863–3886. doi: 10.22436/jnsa.010.07.41. [DOI] [Google Scholar]
  • 27.Wang C., Sakthivel R. Double almost periodicity for high-order Hopfield neural networks with slight vibration in time variables. Neurocomputing. 2018;282:1–15. doi: 10.1016/j.neucom.2017.12.008. [DOI] [Google Scholar]
  • 28.Wang C., Agarwal R.P., O’Regan D. Compactness criteria and new impulsive functional dynamic equations on time scales. Adv. Differ. Equ. 2016;197:1–41. doi: 10.1186/s13662-016-0921-4. [DOI] [Google Scholar]
  • 29.Wang C., Agarwal R.P., O’Regan D. Matrix measure on time scales and almost periodic analysis of the impulsive Lasota-Wazewska model with patch structure and forced perturbations. Math. Meth. Appl. Sci. 2016;39:5651–5669. doi: 10.1002/mma.3951. [DOI] [Google Scholar]
  • 30.Wang C., Agarwal R.P. Almost periodic dynamics for impulsive delay neural networks of a general type on almost periodic time scales. Commun. Nonlinear Sci. Numer. Simulat. 2016;36:238–251. doi: 10.1016/j.cnsns.2015.12.003. [DOI] [Google Scholar]
  • 31.Wang C., Agarwal R.P., O’Regan D., Sakthivel R. Discontinuous generalized double-almost-periodic functions on almost-complete-closed time scales. Bound Value Probl. 2019;165 doi: 10.1186/s13661-019-1283-0. [DOI] [Google Scholar]
  • 32.Wang C., Agarwal R.P. Changing-periodic time scales and decomposition theorems of time scales with applications to functions with local almost periodicity and automorphy. Adv. Differ. Equ. 2015;296:1–21. doi: 10.1186/s13662-015-0633-1. [DOI] [Google Scholar]
  • 33.Wang C., Agarwal R.P., O’Regan D., Sakthivel R. Theory of Translation Closedness for Time Scales. Volume 62 Springer; Cham, Switzerland: 2020. Developments in Mathematics. [Google Scholar]
  • 34.Wang C., Agarwal R.P., O’Regan D. Local-periodic solutions for functional dynamic equations with infinite delay on changing-periodic time scales. Math. Slovaca. 2018;68:1397–1420. doi: 10.1515/ms-2017-0190. [DOI] [Google Scholar]
  • 35.Wang C., Agarwal R.P., O’Regan D., Sakthivel R. Local pseudo almost automorphic functions with applications to semilinear dynamic equations on changing-periodic time scales. Bound Value Probl. 2019;133 doi: 10.1186/s13661-019-1247-4. [DOI] [Google Scholar]
  • 36.Wang C., Agarwal R.P., O’Regan D. A matched space for time scales and applications to the study on functions. Adv. Differ. Equ. 2017;305:1–28. doi: 10.1186/s13662-017-1366-0. [DOI] [Google Scholar]
  • 37.Wang C., Agarwal R.P., O’Regan D. The shift invariance of time scales and applications; Proceedings of the International Workshop QUALITDE-2017; Tbilisi, Georgia. 4–26 December 2017. [Google Scholar]
  • 38.Wang C., Agarwal R.P., O’Regan D. n0-order Δ-almost periodic functions and dynamic equations. Applic. Anal. 2018;97:2626–2654. doi: 10.1080/00036811.2017.1382689. [DOI] [Google Scholar]
  • 39.Wang C., Agarwal R.P., O’Regan D. δ-almost periodic functions and applications to dynamic equations. Mathematics. 2019;7:525. doi: 10.3390/math7060525. [DOI] [Google Scholar]
  • 40.Wang C., Agarwal R.P., O’Regan D. Weighted pseudo δ-almost automorphic functions and abstract dynamic equations. Georgian Math. J. 2019 doi: 10.1515/gmj-2019-2066. in press. [DOI] [Google Scholar]
  • 41.Wang C., Agarwal R.P., O’Regan D., N’Guérékata G.M. n0-Order weighted pseudo Δ-almost automorphic functions and abstract dynamic equations. Mathematics. 2019;7:775. doi: 10.3390/math7090775. [DOI] [Google Scholar]
  • 42.Anirban A. Fuzzy graphene for neuron control. Nat. Rev. Phys. 2020;2:344. doi: 10.1038/s42254-020-0202-8. [DOI] [Google Scholar]
  • 43.Balachandran A.P., Kürkçüoglu S., Vaidya S. Lectures on Fuzzy and Fuzzy SUSY Physics. World Scientific; Singapore: 2007. [Google Scholar]
  • 44.Jarosław P. Quantum Physics, Fuzzy Sets and Logic, Steps Towards a Many-Valued Interpretation of Quantum Mechanics. Springer; Cham, Switzerland: 2015. [Google Scholar]
  • 45.Madore J. Fuzzy physics. Ann. Phys. 1992;219:187–198. doi: 10.1016/0003-4916(92)90316-E. [DOI] [Google Scholar]
  • 46.Adívar M. A new periodic concept for time scales. Math. Slovaca. 2013;63:817–828. doi: 10.2478/s12175-013-0127-0. [DOI] [Google Scholar]
  • 47.Wang C., Agarwal R.P., Sakthivel R. Almost periodic oscillations for delay impulsive stochastic Nicholson’s blowflies timescale model. Comput. Appl. Math. 2018;37:3005–3026. doi: 10.1007/s40314-017-0495-0. [DOI] [Google Scholar]
  • 48.Wang C., Agarwal R.P. Almost periodic solution for a new type of neutral impulsive stochastic Lasota-Wazewska timescale model. Appl. Math. Lett. 2017;70:58–65. doi: 10.1016/j.aml.2017.03.009. [DOI] [Google Scholar]
  • 49.Wang C., Sakthivel R., N’Guérékata G.M. S-almost automorphic solutions for impulsive evolution equations on time scales in shift operators. Mathematics. 2020;8:1028. doi: 10.3390/math8061028. [DOI] [Google Scholar]
  • 50.Wang C., Agarwal R.P., O’Regan D. Calculus of fuzzy vector-valued functions and almost periodic fuzzy vector-valued functions on time scales. Fuzzy Sets Syst. 2019;375:1–52. doi: 10.1016/j.fss.2018.12.008. [DOI] [Google Scholar]
  • 51.Bohner M., Stanzhytskyi O.M., Bratochkina A.O. Stochastic dynamic equations on general time scales. Electron. J. Differ. Equ. 2013;57:1–15. [Google Scholar]
  • 52.Bede B. Mathematics of Fuzzy Sets and Fuzzy Logic. Springer; Heidelberg, Germany: 2013. Studies in Fuzziness and Soft Computing, 295. [Google Scholar]
  • 53.Stefanini L. A generalization of Hukuhara difference and division for interval and fuzzy arithmetic. Fuzzy Sets Syst. 2010;161:1564–1584. doi: 10.1016/j.fss.2009.06.009. [DOI] [Google Scholar]
  • 54.Katz A. Computational Rigid Vehicle Dynamics. Krieger Publishing Co.; Malabar, FL, USA: 1996. [Google Scholar]
  • 55.Kuipers J.B. Quaternions and rotation Sequences: A Primer with Applications to Orbits, Aerospace, and Virtual Reality. Princeton University Press; Princeton, NJ, USA: 1999. [Google Scholar]
  • 56.McCarthy J.M. Introduction to Theoretical Kinematics. MIT Press; Cambridge, MA, USA: 1990. [Google Scholar]
  • 57.Shoemake K. Animating Rotation with Quaternion Curves. Comput. Graph. 1985;19:245–254. doi: 10.1145/325165.325242. [DOI] [Google Scholar]
  • 58.Cheng D., Kou K.I., Xia Y. A unified analysis of linear quaternion dynamic equations on time scales. J. Appl. Anal. Comput. 2018;8:172–201. [Google Scholar]
  • 59.Kou K.I., Xia Y. Linear quaternion differential equations: Basic theory and fundamental results. Studies. Appl. Math. 2018;141:3–45. doi: 10.1111/sapm.12211. [DOI] [Google Scholar]
  • 60.Li Z., Wang C. Cauchy matrix and Liouville formula of quaternion impulsive dynamic equations on time scales. Open Math. 2020;18:353–377. doi: 10.1515/math-2020-0021. [DOI] [Google Scholar]
  • 61.Li Z., Wang C., Agarwal R.P., O’Regan D. Commutativity of quaternion-matrix-valued functions and quaternion matrix dynamic equations on time scales. Stud. Appl. Math. 2021;146:139–210. doi: 10.1111/sapm.12344. [DOI] [Google Scholar]
  • 62.Wang C., Li Z., Agarwal R.P., O’Regan D. Coupled-jumping timescale theory and applications to time-hybrid dynamic equations, convolution and Laplace transforms. Dyn. Syst. Appl. 2021;30:461–508. [Google Scholar]
  • 63.Wang C., Agarwal R.P., O’Regan D. Coupled-Jumping Timescale Theory and Applications; Proceedings of the International Workshop QUALITDE-2020; Tbilisi, Georgia. 19–21 December 2020; pp. 203–208. [Google Scholar]
  • 64.Deniz A., Ufuktepe Ü. Lebesgue–Stieltjes measure on time scales. Turk. J. Math. 2009;33:27–40. [Google Scholar]
  • 65.Deniz A. Master’s Thesis. Graduate School of Engineering and Sciences of Izmir Institute of Technology; Izmir, Turkey: 2007. Measure Theory on Time Scales. [Google Scholar]
  • 66.Sheng Q., Fadag M., Henderson J., Davis J.M. An exploration of combined dynamic derivatives on time scales and their applications. Nonlinear Anal. Real World Appl. 2006;7:395–413. doi: 10.1016/j.nonrwa.2005.03.008. [DOI] [Google Scholar]
  • 67.Li Z., Wang C., Agarwal R.P. The non-eigenvalue form of Liouville’s formula and α-matrix exponential solutions for combined matrix dynamic equations on time scales. Mathematics. 2019;7:962. doi: 10.3390/math7100962. [DOI] [Google Scholar]
  • 68.Wang C., Qin G., Agarwal R.P., O’Regan D. ⋄α-Measurability and combined measure theory on time scales. Appl. Anal. 2020 doi: 10.1080/00036811.2020.1820997. in press. [DOI] [Google Scholar]
  • 69.Qin G., Wang C. Lebesgue–Stieltjes combined ⋄α-measure and integral on time scales. Rev. R. Acad. Cienc. Exactas Fís. Nat. Ser. A Mat. RACSAM. 2021;115:50. doi: 10.1007/s13398-021-01000-y. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data sharing not applicable.


Articles from Entropy are provided here courtesy of Multidisciplinary Digital Publishing Institute (MDPI)

RESOURCES