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. 2022 Apr 19;26(12):5481–5496. doi: 10.1007/s00500-022-07017-8

Limit properties in the metric semi-linear space of picture fuzzy numbers

Nguyen Dinh Phu 1, Nguyen Nhut Hung 2, Ali Ahmadian 3,4,, Soheil Salahshour 5
PMCID: PMC9017969  PMID: 35465468

Abstract

The picture fuzzy set (PFS) just appeared in 2014 and was introduced by Cuong, which is a generalization of intuitionistic fuzzy sets (Atanassov in Fuzzy Sets Syst 20(1):87–96, 1986) and fuzzy sets (Zadeh Inf Control 8(3):338–353, 1965). The picture fuzzy number (PFN) is an ordered value triple, including a membership degree, a neutral-membership degree, a non-membership degree, of a PFS. The PFN is a useful tool to study the problems that have uncertain information in real life. In this paper, the main aim is to develop basic foundations that can become tools for future research related to PFN and picture fuzzy calculus. We first establish a semi-linear space for PFNs by providing two new definitions of two basic operations, addition and scalar multiplication, such that the set of PFNs together with these two operations can form a semi-linear space. Moreover, we also provide some important properties and concepts such as metrics, order relations between two PFNs, geometric difference, multiplication of two PFNs. Next, we introduce picture fuzzy functions with a real domain that is also known as picture fuzzy functions with time-varying values, called geometric picture fuzzy function (GPFFs). In this framework, we give definitions about the limit of GPFFs and sequences of PFN. The important limit properties are also presented in detail. Finally, we prove that the metric semi-linear space of PFNs is complete, which is an important property in the classical mathematical analysis.

Keywords: Picture fuzzy numbers, Metric semi-linear space, Picture fuzzy calculus, Geometric picture fuzzy functions

Introduction

In 1965, Zadeh (1965) first introduced the concept of fuzzy sets (FS) to deal with the uncertainties that appear in many real-world phenomena. It has become the focus of much research in both theoretical and applied fields. In 1986, Atanassov (1986) presented about intuitionistic fuzzy sets (IFS), he came up with the idea of defining a fuzzy set by giving a membership function and a non-member function such that the total between degrees of membership and non-membership is no more than 1. It is a generalization of Zadeh’s fuzzy sets that can be a great idea when describing a problem with a variable language (fuzzy) and is pretty useful in situations when a description of a problem by a linguistic variable is given in terms of a membership function only seems too rough. Due to the flexibility of intuitionistic fuzzy sets in handling uncertainty, they are tools for human consistent reasoning under imperfectly defined facts and vague. In 1984, Takeuti and Titani (1984) by using the same terminology of “intuitionistic fuzzy sets” but with differences in meaning built the concept of intuitionistic fuzzy logic and intuitionistic fuzzy sets. In 1989, Atanassov and Gargov (1989) presented the concept of interval-valued intuitionistic fuzzy sets. In recent years, the intuitionistic fuzzy theory has been applied to many fields, such as decision-making (Atanassov et al. 2005; Chen 2011), medical diagnosis (De et al. 2001; Shinoj and Sunil 2012). Based on the intuitionistic fuzzy sets (IFS), Xu and Yager (2006) introduced the concept of intuitionistic fuzzy number (IFN), which is an ordered non-negative value pair consisting of a membership degree and the non-membership degree of an IFS, and their basic operations. From IFNs and their basic operations, Lei and Xu (2017) developed the fundamental theories of calculus, which are called intuitive fuzzy-calculus. Phu et al (2018, 2019, 2021) continued to develop a semi-linear space for IFNs and introduced its applications.

In 2014, Cuong (2014) introduced the concept of picture fuzzy sets (PFS), which is a generalization of the traditional fuzzy sets (FS) and the intuitionistic fuzzy sets (IFS). The PFS defines a fuzzy set by giving a membership function, a non-member function, and a neutral-membership function with a membership degree, a non-membership degree, and a neutral-membership degree, respectively. Although many uncertain problems in the real world have been effectively handled by the tools of Zadeh’s fuzzy theory and Atanassov’s intuitionistic fuzzy sets, many of them still need the tools of the picture fuzzy theory. Cuong (2014) gave a practical example, which is voting. The idea of the three membership degrees of a PFS can be seen in the case when a voter has to make his or her decision involving more answers like yes, abstain, no. In recent years, there have been many research directions on PFS such as: logical operations and algebraic (Cuong 2014; Cuong and Kreinovich 2013; Dutta and Ganju 2017), fuzzy clustering (Son 2016; Thong and Son 2015), decision-making (Khan et al. 2019; Si et al. 2019; Wei 2017), nonlinear programming Phu et al. (2021). As in a similar way, based on the PFS, we also get the definition of picture fuzzy number (PFN) as an ordered non-negative triple consisting of a membership degree, a non-membership degree, and a neutral-membership degree of a PFS. A PFN is a basic element of a PFS and is a useful tool to help us study more deeply the characteristics and properties of PFS. The PFNs are used to study decision-making theories in the picture fuzzy environment. For example, Wei (2017) studied multiple attribute decision-making (MADM) problems based on picture fuzzy information in the form of PFNs. Khan et al. (2019) presented a logarithmic approach to MADM problems with PFNs.

As we know along with the development of logic and algebraic theory for fuzzy-theory, then calculus-theory also had many powerful strides in recent years. For Zadeh’s fuzzy theory, the calculus-theory in this fuzzy environment is also known by the name, fuzzy mathematics. In which, fuzzy numbers [can see Diamond and Kloeden (2000)] are basic and the main tool to develop for this field. For intuitionistic fuzzy theory, Lei and Xu (2017) used IFN as a tool for developing calculus theory in an intuitionistic fuzzy environment. For picture fuzzy-theory, development for the calculus-theory in picture fuzzy environments is still very new. Because basic operations (most importantly, addition and scalar multiplication) in Zadeh’s fuzzy environment and Atanassov’s intuitionistic environment have been defined and perfected in recent years [(can see in Dubois and Prade (1982), Phu et al. (2019), Xu and Yager (2006)], it has helped calculus theory that has a basis for development. Meanwhile, there are not perfect definitions for the basic operations in the picture fuzzy environment. Although Wei (2017) provided the basic operations of picture fuzzy numbers based on Xu’s operation Xu and Yager (2006) for intuitionistic fuzzy number, some of them, namely scalar multiplication and addition, have some limitations. In this paper, we will show the limitation of Wei’s two operations, addition, and scalar multiplication, and will provide two new operations with more advantages. The highlight is that the set of picture fuzzy numbers together with these two new operations can become a semi-linear space. This will be the framework for the development of future studies on picture fuzzy calculus theory. At the same time, we define the metric space for PFNs and present the concepts of limits and their properties because the limit is an important basis of picture fuzzy calculus. Finally, to confirm that the limit operators are well-defined, we have verified the completeness in metric space of PFNs.

The paper is organized as follows: In Sect. 2, we recall some knowledge to prepare for the next section. In Sect. 3, we divide the content into two subsections: For the first subsection, we point out some limitations in Wei’s two operations and proceed to construct two new operations, addition and scalar multiplication, such that the set of PFNs together these operations becomes a semi-linear space for PFNs. On the other hand, we also present some related concepts and their properties such as order relations between two PFNs, metrics, geometric difference, multiplication of two PFNs. For the second subsection, we first define a function whose value changes over time, called the geometric picture fuzzy function. Next, we introduce the definition of limit for this function and the sequence of PFNs. Their properties are also shown. Finally, we prove that the metric space of PFNs is complete.

Preliminaries

For a start, we recall the concept of picture fuzzy sets, which was introduced by Cuong (2014).

Definition 2.1

(Cuong 2014) Let Ω be a universe set, then a set called a picture fuzzy set (PFS), which is defined as follows:

graphic file with name 500_2022_7017_Equ69_HTML.gif

where Inline graphic is called a membership function, νX:Ω[0,1] is called a non-membership function, ηX:Ω[0,1] is called a neutral-membership function with a membership degree μX(ω), a non-membership degree νX(ω) and a neutral-membership degree of element ωΩ, respectively, such that

0μX(ω)+ηX(ω)+νX(ω)1.

Definition 2.2

a picture fuzzy number (PFN) x, which is defined as follows:

x=μx,ηx,νx

where μx,ηx, and νx are nonnegative real numbers such that 0μx,ηx,νx1 and

0μx+ηx+νx1.

PFNs have been researched in recent years. For example, Wei (2017) researched multiple attribute decision-making (MADM) problems based on picture fuzzy information in the form of PFNs. He developed picture fuzzy aggregation operators for PFNs from geometric and arithmetic operations, then he used them to solve the picture fuzzy MADM problems. Khan et al. (2019) presented a logarithmic approach to MADM problem with picture fuzzy information in the form of PFNs. They developed a series of picture fuzzy logarithmic aggregation operators for PFNs and provided a novel algorithm technique to solve the MADM problems with picture fuzzy information. Si et al. (2019) provided a method for comparing and ranking PFNs. Furthermore, Wei (2017) also defined some basic operators of PFNs as follows:

Definition 2.3

(Wei 2017) Let x=μx,ηx,νx and y=μy,ηy,νy be two PFNs, then

  1. x¯=νx,ηx,μx is called the reverse element of x

  2. xy=μx+μy-μxμy,ηxηy,νxνy;

  3. xy=μxμy,ηx+ηy-ηxηy,νx+νy-νxνy;

  4. λx=1-(1-μx)λ,ηxλ,νxλ with λ>0;

  5. xλ=μxλ,1-(1-ηx)λ,1-(1-νx)λ with λ>0;

  6. xy=min{μx,μy},max{ηx,ηy},max{νx,νy};

  7. xy=max{μx,μy},min{ηx,ηy},min{νx,νy}.

Next, we recall the common definition of a linear space (or vector space) over a scalar field (which may be real or complex).

Definition 2.4

Let F be a scalar field, then a linear space (or vector space) over the field F is a set A together with two operations, which addition and scalar multiplication are defined:

(addition)+:A×AAx,yAx+yA(scalar multiplication)·:F×AAλF,xAλxA

that satisfy the eight axioms listed below. Let x,y, and z be belong to A,  and λ and β scalars in F.

  1. x+(y+z)=(x+y)+z;

  2. x+y=y+x;

  3. x+0=x with 0A, called a neutral element of A

  4. x+(x¯)=0 with x¯A, called a reverse element of x

  5. λ(βx)=(λβ)x;

  6. 1x=x, which 1 is identity element of F

  7. λ(x+y)=λx+λy;

  8. (λ+Fβ)x=λx+Fβx, where +F is addition of the field F.

Remark 2.5

In fact, there are many sets with their two operations (addition and scalar multiplication) that do not satisfy the axiom in item (4.) of Definition 2.4. For example, for the set of interval numbers, let X=[X_,X¯] be a interval number with X_<X¯ and -X=[-X¯,-X_] be reverse element of X,  then X+(-X)0=[0,0] [(can see in Moore et al. (2009)]. The same for the set of Zadeh’s fuzzy numbers, let ω=(a,b,c) be a triangular fuzzy number with a<b<c and -ω=(-c,-b,-a) be also reverse element of ω, then ω+(-ω)0=(0,0,0) [can see in Dijkman et al. (1983)]. If the axiom in item (4.) of Definition 2.4 does not satisfy, which is x+(x¯)0, then the set A will be called a semi-linear space. We provide the following definition for a semi-linear space [can see in Galanis (2009); Phu et al. (2019); Worth (1970)] with scalar field F, which is the real number field R.

Definition 2.6

(Galanis 2009; Phu et al. 2019; Worth 1970) A semi-linear space is a set B together two operations, addition and scalar multiplication with nonnegative reals, are defined:

  • The first operation: addition, denoted by +, such that to every pair x,yB, there correspond a element x+yB,

  • The second operation: scalar multiplication of xB with an element λR+, denoted by λxB,

such that satisfy the following properties for every x,yB and λ,βR+ :

  1. x+(y+z)=(x+y)+z;

  2. x+y=y+x;

  3. x+0=x with 0B, called a neutral element of B

  4. λ(x+y)=λx+λy;

  5. (λ+Rβ)x=λx+Rβx, where +R is addition of the field R

  6. (λβ)x=λ(βx);

  7. 1x=x and 0x=0 with 1R+ and 0R+ are identity element and neutral element of scalar multiplication, respectively.

Remark 2.7

From Definition 2.6 and Remark 2.5, we can see that the set of interval numbers and the set of fuzzy numbers in Zadeh’s sense together their two operations (addition and scalar multiplication) are semi-linear spaces. We recently defined the new addition and scalar multiplication for intuitionistic fuzzy numbers (IFNs), which are the basic elements of Atanassov’s fuzzy sets Atanassov (1986) so that the set of IFNs becomes a semi-linear space [(can see in Phu and Hung (2018); Phu et al. (2019)]. In the next section, we will extend these two operations for picture fuzzy numbers (PFNs), which are also the basic elements of picture fuzzy sets, such that the set of PFNs also becomes a semi-linear space.

Definition 2.8

(Rudin 1976) A metric space is a set C together a metric or a distance function, which is defined:

a mappingδ:C×CRx,yCδ(x,y)R

such that satisfy the following properties for every x,yC:

  1. δ(x,y)>0 if xy and δ(x,y)=0 if x=y;

  2. δ(x,y)=δ(y,x);

  3. δ(x,y)δ(x,z)+δ(z,y) with zC

Definition 2.9

(Rudin 1976) Let xDR, Suppose the real-valued function f(x) is defined when x is near the number x0D. Then, we define limxx0f(x)=L, or f(x)L as xx0, and say that the limit of f(x),  as x approaches x0, equals L. Simultaneously, limxx0f(x)=L if and only if for every ε>0 there is a number δ>0 such that if 0|x-x0|δ then |f(x)-L|ε.

Definition 2.10

(Rudin 1976) Let {xn}R be a sequence of n real numbers with nN. Then, we define that the sequence {xn} has the limit L and is denoted by limn+xn=L or xnL as n+. Simultaneously, if for every ε>0 there is a positive integer N such that if n>N then |xn-L|ε.

Main result

The metric semi-linear space for PFNs

In this subsection, we will introduce some new concepts and definitions such as a set of PFNs, a neutral element and a reverse element of this set, new addition and scalar multiplication for PFNs. Then, we will prove this set together the new two operations to become a semi-linear space by verifying that these two new operations satisfy the seven axioms in Definition 2.6.

For convenience, we put x=(x1,x2,x3) instead of x=μx,ηx,νx in Definition 2.2.

Definition 3.1

Let x=(x1,x2,x3) be an any PFN, then the following set

Dg=x=(x1,x2,x3)|0x1+x2+x31, 3.1

is called the set of PFNs. Where x1=μx[0,1] correspond to a membership degree of x, x2=ηx[0,1] correspond to neutral-membership degree of x,  and x3=νx[0,1] correspond to non-membership degree of x.

In this study, we describe a PFN as an ordered non-negative triples (x1,x2,x3) in Dg[0,1]3. In addition, any PFN x=(x1,x2,x3), we have a box BxDg and is defined as the following form:

Bx=(v,w,u)|0vx1,0wx2,0ux3.

Illustrations for Dg and an element x=(x1,x2,x3) in Dg are shown in Figs. 1 and 2 .

Fig. 1.

Fig. 1

The image illustrating for Dg, which is the set of PFNs and where the PFSs get values

Fig. 2.

Fig. 2

The box Bx and a geometric interpretation of PFN x=(x1,x2,x3) in Dg

Definition 3.2

The reverse element of x=(x1,x2,x3) in Dg is the element x¯=(x3,x2,x1) in Dg. Simultaneously, the neutral element in Dg is θ=(0,0,0).

For two base operations of PFNs, addition and scalar multiplication, Wei provided these two operations in item (2.) and (4.) of Definition 2.3 [(can see in Wei (2017)]. However, they have many limitations to help the set of PFNs that become a semi-linear space. To see these limitations, we will test these two operations with the seven axioms in Definition 2.6.

Proposition 3.3

Let two operations, addition and scalar multiplication with nonnegative reals, be defined:

  • The addition: every two PFNs x,yDg with x=(x1,x2,x3) and y=(y1,y2,y3), there correspond a element xyDg and xy=x1+y1-x1y1,x2y2,x3y3, where addition is denoted by ,

  • The scalar multiplication: for a PFN xDg with λ>0, there correspond a element λxDg and λx=1-(1-x1)λ,x2λ,x3λ.

Then, they satisfy the following properties for every x,y,zDg and λ,β>0 :

  1. x(yz)=(xy)z;

  2. xy=yx;

  3. λ(xy)=λxλy;

  4. (λβ)x=λ(βx);

  5. 1x=x.

Proof

Let x=(x1,x2,x3), y=(y1,y2,y3), and z=(z1,z2,z3) be the PFNs and λ,β>0. We obtain

(1.)x(yz)=xy1+z1-y1z1,y2z2,y3z3=x1+(y1+z1-y1z1)-x1(y1+z1-y1z1),x2y2z2,x3y3z3=x1+y1+z1-y1z1-x1y1-x1z1+x1y1z1,x2y2z2,x3y3z3=(x1+y1-x1y1)+z1-z1(x1+y1-x1y1),x2y2z2,x3y3z3=(xy)z;
(2.)xy=x1+y1-x1y1,x2y2,x3y3=y1+x1-y1x1,y2x2,y3x3=yx;
(3.)λ(xy)=λx1+y1-x1y1,x2y2,x3y3=1-(1-x1-y1+x1y1)λ,(x2y2)λ,(x3y3)λ=1-((1-x1)(1-y2))λ,(x2y2)λ,(x3y3)λ=1-(1-x1)λ(1-y2)λ,x2λy2λ,x3λy3λ=1+[1-(1-x1)λ-(1-y2)λ-1+(1-x1)λ+(1-y2)λ]-(1-x1)λ(1-y2)λ,x2λy2λ,x3λy3λ=1-(1-x1)λ,x2λ,x3λ1-(1-y1)λ,y2λ,y3λ=λxλy;
(4.)(λβ)x=1-(1-x1)λβ,x2λβ,x3λβ=1-(1-x1)λβ,x2λβ,x3λβ=1-(1-1+(1-x1))βλ,x2βλ,x3βλ=λ(βx);

(5.)1x=1-(1-x1)1,x21,x31=x1,x2,x3=x.

Remark 3.4

In Proposition 3.3, we show the five axioms that Wei’s two operations satisfy in the seven axioms of Definition 2.6. We now analyze the limitations of these two operations. Firstly, (Dg,) is not a commutative cancellation semi-group with its neutral element because the Dg together the addition in Proposition 3.3 has no a neutral element in Dg. This means that there is no neutral element 0 in Dg such that x0=x with xDg. Indeed, let us assume that there exists a 0 element in Dg such that x0=x with 0=μ0,η0,ν0 and x=(x1,x2,x3). We get

x1+μ0-x1μ0=x1x2η0=x2x3ν0=x3μ0=0η0=1ν0=1

However, 0 does not belong to Dg since μ0+η0+ν0=2>1, this contradicts the original assumption. Therefore, the neutral element does not exist in (Dg,). Secondly, because 0=(0,1,1)Dg, 0xDg, where 0 is neutral element of field R. Indeed, we see that 0x=1-(1-x1)0,x20,x30=(0,1,1)=0. Finally, the scalar multiplication in Proposition 3.3 does not satisfy the axiom “distributivity of scalar multiplication concerning field addition”, which means that the axiom (v) of Definition 2.6 does not satisfy. Indeed, let x=(x1,x2,x3) be a PFN and λ,β>0. Then,

(λ+Rβ)x=1-(1-x1)(λ+Rβ),x2(λ+Rβ),x3(λ+Rβ).=1-(1-x1)(λ+β),x2(λ+β),x3(λ+β).λx+Rβx=1-(1-x1)λ,x2λ,x3λ+R1-(1-x1)β,x2β,x3β=2-(1-x1)λ-(1-x1)β,x2λ+x2β,x3λ+x3β.

Therefore, (λ+Rβ)xλx+Rβx in general. For example, let x=(0.25,0.25,0.25) and λ=β=2. We get

(2+2)x=1-(1-0.25)4,0.254,0.254=(0.6836,0.0039,0.0039).2x+2x=1-(1-0.25)2,0.252,0.252+1-(1-0.25)2,0.252,0.252=(0.4375,0.0625,0.0625)+(0.4375,0.0625,0.0625)=(0.875,0.125,0.125)

and (2+2)x2x+2x. Thus, from the above limitations, we come to the conclusion that the set Dg together two base operations, addition and scalar multiplication, in Proposition 3.3 is not semi-linear space. Therefore, we will provide new two operations that make Dg to become a semi-linear space in what follows.

Definition 3.5

Let m elements x(1),x(2),x(3),,x(m) in Dg. Then, the geometric addition, denoted by g, of these m elements is a PFN y=x(1)gx(2)g...gx(m), if it exists, such that

y=x1(1)+x1(2)++x1(m)2m-p-1,x2(1)+x2(2)++x2(m)2m-p-1,x3(1)+x3(2)++x3(m)2m-p-1,

where p is the number of elements x(k)=(0,0,0)=θ,k=1,2,...,m and satisfy

(a)x(1)gx(2)g...gx(n)gx(n+1)g....gx(m)=x1(1)+x1(2)++x1(m)2m-p-1,x2(1)+x2(2)++x2(m)2m-p-1,x3(1)+x3(2)++x3(m)2m-p-1,(b)x(1)gx(2)g...gx(n-1)gx(n)g....gx(m)=x1(1)+x1(2)++x1(m)2m-p-1,x2(1)+x2(2)++x2(m)2m-p-1,x3(1)+x3(2)++x3(m)2m-p-1,

with nm.

Corollary 3.6

If the geometric addition in Definition 3.5 of m elements x(1),x(2),x(3),,x(m) in Dg exists, then

  1. We have a binary addition operation of two elements x=(x1,x2,x3) and y=(y1,y2,y3) in Dg as follows:
    xgy=x1+y121-p,x2+y221-p,x3+y321-p, 3.2
    where p is the number of neutral elements θ=(0,0,0) in this addition, p=2 when x=y=θ, p=1 when x=θ,yθ or xθ,y=θ, and p=0 when xθ and yθ.
  2. Let x=(x1,x2,x3), y=(y1,y2,y3), and z=(z1,z2,z3) be PFNs, then we have
    (xgy)gz=xg(ygz).

Proof

Let us suppose that the geometric addition in Definition 3.5 of m elements x(1),x(2),x(3),,x(m) in Dg exists.

(1) with m=2, we get

x(1)gx(2)=x1(1)+x1(2)22-p-1,x2(1)+x2(2)22-p-1,x3(1)+y3(2)22-p-1=x1(1)+x1(2)21-p,x2(1)+x2(2)21-p,x3(1)+y3(2)21-p,

we substitute x(1)=x=(x1,x2,x3) and x(2)=y=(y1,y2,y3). Thus, we obtain

xgy=x1+y121-p,x2+y221-p,x3+y321-p.

(2) From conditions (a) and (b) with m=3 and n=2, we obtain

x(1)gx(2)gx(3)=x1(1)+x1(2)+x1(3)23-p-1,x2(1)+x2(2)+x2(3)23-p-1,x3(1)+x3(2)+x3(3)23-p-1x(1)gx(2)gx(3)=x1(1)+x1(2)+x1(3)23-p-1,x2(1)+x2(2)+x2(3)23-p-1,x3(1)+x3(2)+x3(3)23-p-1x(1)gx(2)gx(3)=x(1)gx(2)gx(3)

we substitute x(1)=x=(x1,x2,x3), x(2)=y=(y1,y2,y3), x(3)=z=(z1,z2,z3) and the proof is completed.

Theorem 3.7

Let m elements x(1),x(2),x(3),,x(m) in Dg. Then, there is an element y in Dg such that y=x(1)gx(2)g...gx(m) with

y=x1(1)+x1(2)++x1(m)2m-p-1,x2(1)+x2(2)++x2(m)2m-p-1,x3(1)+x3(2)++x3(m)2m-p-1,

where p is the number of elements x(k)=(0,0,0)=θ,k=1,2,...,m.

Proof

We will prove this theorem by mathematical induction method. Indeed, with m=1, this theorem is true because we always have x=(x1,x2,x3)Dg and 0x1+x2+x31. Next, let us assume that this theorem is also true for m-1 elements x(1),x(2),x(3),,x(m-1) in Dg. Finally, we need to prove the theorem is true for m elements. From inductive assumption, we obtain an element z in Dg such that z=x(1)gx(2)g...gx(m-1) with

z=x1(1)+x1(2)++x1(m-1)2(m-1)-p-1,x2(1)+x2(2)++x2(m-1)2(m-1)-p-1,x3(1)+x3(2)++x3(m-1)2(m-1)-p-1,

where p is the number of elements x(k)=(0,0,0)=θ,k=1,2,...,m-1. Because zDg, we have

0x1(1)+x1(2)++x1(m-1)2(m-1)-p-1+x2(1)+x2(2)++x2(m-1)2(m-1)-p-1+x3(1)+x3(2)++x3(m-1)2(m-1)-p-11. 3.3

To prove an element y in Dg such that y=x(1)gx(2)g...gx(m-1)gx(m) with

y=x1(1)+x1(2)++x1(m)2m-p-1,x2(1)+x2(2)++x2(m)2m-p-1,x3(1)+x3(2)++x3(m)2m-p-1,

where p is the number of elements x(k)=(0,0,0)=θ,k=1,2,...,m-1,m. We need to show that

0x1(1)+x1(2)++x1(m)2m-p-1+x2(1)+x2(2)++x2(m)2m-p-1+x3(1)+x3(2)++x3(m)2m-p-11.

Indeed, with case p=m: We have

x(1)=x(2)=x(3)==x(m-1)=x(m)=θ=(0,0,0)y=x(1)gx(2)gx(3)ggx(m-1)gx(m)=θ=(0,0,0)y=θDg.

With case p=m-1: We have

x(1)=x(2)==x(j-1)=x(j+1)==x(m)=θ=(0,0,0)andx(j)θ,j1,m¯y=0++x1(j)++02m-(m-1)-1,0++x2(j)++02m-(m-1)-1,0++x3(j)++02m-(m-1)-1=x1(j),x2(j),x3(j)y=x(1)gx(2)gx(3)ggx(m-1)gx(m)=x(j)y=x(j)Dgwithj1,m¯.

With case 0pm-2: From Eq 3.4, we have (Fig. 3)

0x1(1)+x1(2)++x1(m-1)2(m-1)-p-1+x2(1)+x2(2)++x2(m-1)2(m-1)-p-1+x3(1)+x3(2)++x3(m-1)2(m-1)-p-11.0x1(1)+x1(2)++x1(m-1)+(x1(m)-x1(m))2(m-1)-p-1+(1-1)+x2(1)+x2(2)++x2(m-1)+(x2(m)-x2(m))2(m-1)-p-1+(1-1)+x3(1)+x3(2)++x3(m-1)+(x3(m)-x3(m))2(m-1)-p-1+(1-1)1.0x1(1)+x1(2)++x1(m-1)+(x1(m)-x1(m))2m-p-1.2-1+x2(1)+x2(2)++x2(m-1)+(x2(m)-x2(m))2m-p-1.2-1+x3(1)+x3(2)++x3(m-1)+(x3(m)-x3(m))2m-p-1.2-11.0x1(1)+x1(2)++x1(m-1)+(x1(m)-x1(m))2m-p-1+x2(1)+x2(2)++x2(m-1)+(x2(m)-x2(m))2m-p-1+x3(1)+x3(2)++x3(m-1)+(x3(m)-x3(m))2m-p-112.0x1(1)+x1(2)++x1(m-1)+x1(m)2m-p-1+x2(1)+x2(2)++x2(m-1)+x2(m)2m-p-1+x3(1)+x3(2)++x3(m-1)+x3(m)2m-p-1x1(m)+x2(m)+x3(m)2m-p-1+12.0x1(1)+x1(2)++x1(m-1)+x1(m)2m-p-1+x2(1)+x2(2)++x2(m-1)+x2(m)2m-p-1+x3(1)+x3(2)++x3(m-1)+x3(m)2m-p-112m-p-1+121.

So the proof is completed.

Fig. 3.

Fig. 3

Geometric interpretation of binary addition operation xGy=zDg of the GPFNs in Definition 3.5, with both x and y are different θ

Definition 3.8

Let x=(x1,x2,x3) be a PFN and λR+, then the scalar multiplication, denoted by g, is a PFN y=λgx such that

y=λx1,λx2,λx3

and 0λx1+λx2+λx31.

In our first idea, when we came up with this scalar multiplication, we only consider λ[0,1], because we want to make sure λgxDg. However, we realize that there are many cases λR+ (maybe expand further), but the results still belong to Dg, for a simple example, with λ=10 and x=(x1,x2,x3)=(0.004,0.0065,0.0009)Dg then λgx=(λ.x1,λ.x2,λ.x3)=(10×0.004,10×0.0065,10×0.0009)=(0.04,0.065,0.009)=(z1,z2,z3)=z, we can see that z belongs to Dg because 0z1+z2+z31.

Theorem 3.9

Let x=(x1,x2,x3), y=(y1,y2,y3), and z=(z1,z2,z3) be three PFNs and λ,βR+, then

  1. xgy=ygx;

  2. (xgy)gz=xg(ygz);

  3. xgθ=x;

  4. λg(xgy)=λgxgλgy;

  5. (λ+β)gx=λgx+βgx;

  6. (λ.β)gx=λg(βgx);

  7. 1gx=x and 0gx=θ.

Proof

From the addition in Definition 3.5 and scalar multiplication in Definition 3.8, we have:

  1. According to the item (i) of Corollary 3.6, we get
    xgy=x1+y121-p,x2+y221-p,x3+y321-p=y1+x121-p,y2+x221-p,y3+x321-p=ygx.
  2. This is the result of the item (ii) of Corollary 3.6.

  3. According to the item (i) of Corollary 3.6 with xθ=(0,0,0). Because in the formula xgθ, there is a zero element (θ=(0,0,0)Dg) so p=1 and we get
    xgθ=x1+021-1,x2+021-1,x3+021-1=x1,x2,x3=x.
  4. According to the item (i) of Corollary 3.6 and scalar multiplication in Definition 3.8, we get
    λg(xgy)=λgx1+y121-p,x2+y221-p,x3+y321-p=λx1+y121-p,λx2+y221-p,λx3+y321-p=λx1+λy121-p,λx2+λy221-p,λx3+λy321-p=λgxgλgy.
  5. According to the scalar multiplication in Definition 3.8, we get
    (λ+β)gx=(λ+β)x1,(λ+β)x2,(λ+β)x3=λx1+βx1,λx2+βx2,λx3+βx3=λx1,λx2,λx3+βx1,βx2,βx3=λgx+βgx.
  6. According to the scalar multiplication in Definition 3.8, we get
    (λβ)gx=(λβ)x1,(λβ)x2,(λβ)x3=λβx1,λβx2,λβx3=λgβx1,βx2,βx3=λg(βgx)
  7. According to the scalar multiplication in Definition 3.8, we get 1gx=1.x1,1.x2,1.x3=(x1,x2,x3)=x and 0gx=0.x1,0.x2,0.x3=(0,0,0)=θ.

The theorem has completed proof.

Theorem 3.10

Let the set of PFNs Dg in Definition 3.1, the geometric addition g in Definition 3.5 and the scalar multiplication g in Definition 3.8. Then, (Dg,g,g) is a semi-linear space.

Proof

From Definition 2.6, we see that Theorem 3.10 is a direct result of Theorem 3.9.

Theorem 3.11

Let a mapping, denoted by HDg, be defined:

HDg:Dg×DgR(x,y)HDg(x,y)=supi1,3¯xi-yi.

Then, Dg,HDg is a metric semi-linear space.

Proof

First of all, we need to prove that the mapping HDg is a metric on Dg. Indeed, for any x,yDg with x=(x1,x2,x3) and y=(y1,y2,y3), we have

  1. HDg(x,y)=supx1-y1,x2-y2,x3-y3>0
    with xy and if x=y,
    HDg(x,y)=supx1-y1,x2-y2,x3-y3=sup0,0,0=0;
  2. HDg(x,y)=supi1,3¯xi-yi=supi1,3¯yi-xi=HDg(y,x);

  3. HDg(x,y)=supi1,3¯xi-yi=supi1,3¯xi-zi+zi-yisupi1,3¯xi-zi+zi-yisupi1,3¯xi-zi+supi1,3¯zi-yi=HDg(x,z)+HDg(z,y)withz=(z1,z2,z3)Dg.

From Definition 2.8, we obtain that the mapping HDg is a metric on Dg. Thus, Dg,HDg is a metric space.

Definition 3.12

Let x=(x1,x2,x3) and y=(y1,y2,y3) be two PFNs. We define the order relations between these two PFNs as follows:

  • Type 1:

    x1y in Dg iff it satisfies x1y1, x2y2, and x3y3. And, D1=(x,y)Dg×Dg|x1y.

  • Type 2:

    x2y in Dg iff it satisfies y1x1, x2y2, and x3y3. And, D2=(x,y)Dg×Dg|x2y.

  • Type 3:

    x3y in Dg iff it satisfies x1y1, y2x2, and x3y3. And, D3=(x,y)Dg×Dg|x3y.

  • Type 4:

    x4y in Dg iff it satisfies x1y1, x2y2, and y3x3. And, D4=(x,y)Dg×Dg|x4y.

  • Type 5:

    x5y in Dg iff it satisfies y1x1, y2x2, and x3y3. And, D5=(x,y)Dg×Dg|x5y.

  • Type 6:

    x6y in Dg iff it satisfies y1x1, x2y2, and y3x3. And, D6=(x,y)Dg×Dg|x6y.

  • Type 7:

    x7y in Dg iff it satisfies x1y1, y2x2, and y3x3. And, D7=(x,y)Dg×Dg|x7y.

  • Type 8:

    x8y in Dg iff it satisfies y1x1, y2x2, and y3x3. And, D8=(x,y)Dg×Dg|x8y.

Definition 3.13

Let x=(x1,x2,x3) and y=(y1,y2,y3) be two PFNs. We define the geometric difference between these two PFNs as follows:

  • Case 1 If (x,y)D1 and 0i=13|yi-xi|1, there correspond a element yg1xDg and yg1x=y1-x1,y2-x2,y3-x3, where geometric difference denoted by g1;

  • Case 2 If (x,y)D2 and 0i=13|yi-xi|1, there correspond a element yg2xDg and yg2x=x1-y1,y2-x2,y3-x3, where geometric difference denoted by g2;

  • Case 3 If (x,y)D3 and 0i=13|yi-xi|1, there correspond a element yg3xDg and yg3x=y1-x1,x2-y2,y3-x3, where geometric difference denoted by g3;

  • Case 4 If (x,y)D4 and 0i=13|yi-xi|1, there correspond a element yg4xDg and yg4x=y1-x1,y2-x2,x3-y3, where geometric difference denoted by g4;

  • Case 5 If (x,y)D5 and 0i=13|yi-xi|1, there correspond a element yg5xDg and yg5x=x1-y1,x2-y2,y3-x3, where geometric difference denoted by g5;

  • Case 6 If (x,y)D6 and 0i=13|yi-xi|1, there correspond a element yg6xDg and yg6x=x1-y1,y2-x2,x3-y3, where geometric difference denoted by g6;

  • Case 7 If (x,y)D7 and 0i=13|yi-xi|1, there correspond a element yg7xDg and yg7x=y1-x1,x2-y2,x3-y3, where geometric difference denoted by g7;

  • Case 8 If (x,y)D8 and 0i=13|yi-xi|1, there correspond a element yg8xDg and yg8x=x1-y1,x2-y2,x3-y3, where geometric difference denoted by g8.

In conclusion, every two PFNs (x,y)k=18Dk and there exists a element ygkxDg with k{1,2,3,4,5,6,7,8}, then we say that there exists a geometric difference ygx (with symbol g).

Definition 3.14

Let x=(x1,x2,x3) and y=(y1,y2,y3) be any two PFNs. Then, the geometric difference, denoted by g, between these two PFNs is a PFN z=ygx, if it exists, such that

z=|y1-x1|,|y2-x2|,|y3-x3|

Theorem 3.15

The concepts of geometric difference in Definition 3.13 and in Definition 3.14 are the same.

Proof

Let x=(x1,x2,x3) and y=(y1,y2,y3) be any two PFNs. Assume that the geometric difference between these two PFNs in Definition 3.17 exists, this means that there is a PFN z=ygx belonging to Dg and z=|y1-x1|,|y2-x2|,|y3-x3|. Because zDg, we have 0i=13|yi-xi|1 and

  • Case 1 z=y1-x1,y2-x2,y3-x3 if x1y1, x2y2, and x3y3 are equivalent to (x,y)D1;

  • Case 2 z=x1-y1,y2-x2,y3-x3 if y1x1, x2y2, and x3y3 are equivalent to (x,y)D2;

  • Case 3 z=y1-x1,x2-y2,y3-x3 if x1y1, y2x2, and x3y3 are equivalent to (x,y)D3;

  • Case 4 z=y1-x1,y2-x2,x3-y3 if x1y1, x2y2, and y3x3 are equivalent to (x,y)D4;

  • Case 5 z=x1-y1,x2-y2,y3-x3 if y1x1, y2x2, and x3y3 are equivalent to (x,y)D5;

  • Case 6 z=x1-y1,y2-x2,x3-y3 if y1x1, x2y2, and y3x3 are equivalent to (x,y)D6;

  • Case 7 z=y1-x1,x2-y2,x3-y3 if x1y1, y2x2, and y3x3 are equivalent to (x,y)D7;

  • Case 8 z=x1-y1,x2-y2,x3-y3 if y1x1, y2x2, and y3x3 are equivalent to (x,y)D8.

Therefore, the concept of geometric difference in Definition 3.13 and Definition 3.14 is equivalent.

Theorem 3.16

Let x=(x1,x2,x3) and y=(y1,y2,y3) be two PFNs. Then, we have the following properties:

  1. xgx=θ;

  2. If z=ygx exists, it is unique;

  3. If ygx exists, then xgy exists and ygx=xgy;

  4. If ygx=xgy=θ, then y=x;

Proof

For property (1), we have xgx=|x1-x1|,|x2-x2|,|x3-x3|=(0,0,0)=θ. For property (ii), assume that we have two PFNs h=ygx, g=ygx and hg, then |y1-x1|,|y2-x2|,|y3-x3||y1-x1|,|y2-x2|,|y3-x3|. For convenience, we rewrite |yi-xi||yi-xi| with i{1,2,3}. Case 1, if xiyi, then

|yi-xi||yi-xi|yi-xiyi-xi00withi{1,2,3}.

Case 2, if yixi, then

|yi-xi||yi-xi|xi-yixi-yi00withi{1,2,3}.

both cases 1 and 2 contradict the above assumption. Thus, we get h=g. For property (3), suppose that ygx exists, we have

ygx=|y1-x1|,|y2-x2|,|y3-x3|=|x1-y1|,|x2-y2|,|x3-y3|=xgy.

To property (iv), suppose that ygx=θ, we have

|y1-x1|,|y2-x2|,|y3-x3|=(0,0,0)|yi-xi|=0yi=xiwithi{1,2,3}.

Thus, we get x=y.

Definition 3.17

Let x and y be two PFNs with x=(x1,x2,x3) and y=(y1,y2,y3), then there correspond a element xgyDg and xgy=x1y1,x2y2,x3y3, where multiplication of two PFNs is denoted by g.

Theorem 3.18

g in Definition 3.17 is well defined, i.e., let x and y be two PFNs, then xgy also is a PFN.

Proof

To prove this theorem, we need to prove that if a[0,1] and b[0,1], where a and b are two real numbers then a.b[0,1]. Indeed, Putting a=nm and b=pq, which n,m,p, and q are positive integers. Since a[0,1] and b[0,1], we obtain nm and pq. Simultaneously, we also have n.pm.q because they are positive integers. Thus, we obtain a.b=nm.pq=n.pm.q[0,1]. Hence, let x and y be two PFNs with x=(x1,x2,x3) and y=(y1,y2,y3), we have 0x1+x2+x31, 0y1+y2+y31, and 0(x1+x2+x3).(y1+y2+y3)1. With (x1+x2+x3).(y1+y2+y3)=i=13j=13xiyj, we have

0x1y1+x2y2+x3y3i=13j=13xiyj1.

Furthermore, since xi[0,1] and yi[0,1] with i{1,2,3}, we get xiyi[0,1]. Therefore, xgy=x1y1,x2y2,x3y3 is a PFN.

The geometric picture fuzzy functions

In this subsection, we study the picture fuzzy functions (PFFs), which are the functions related to PFNs, with a real domain. Let

f:IRDgtf(t)=f1(t),f2(t),f3(t),

where 0f1(t)+f2(t)+f3(t)1. We call f(t) is geometric picture fuzzy functions (GPFFs) in Dg. In classical mathematics, the limit of a function or sequence of numbers is a fundamental concept in calculus and analysis that involves the behavior of that function or sequence of numbers near a particular input. It is the main tool for the development of important properties in the theory of calculus such as continuity, differentiable, integrable, etc. In the following, we will present definitions and properties for the limit of GPFFs and the sequence of PFNs in detail.

Definition 3.19

Let f(t)=f1(t),f2(t),f3(t) be a GPFF for tIR. If the limits of component functions exist, i.e., limtt0f1(t)=L1, limtt0f2(t)=L2, and limtt0f3(t)=L3, such that L1,L2,L3Dg , then we define the limit of f(t) as follows:

limtt0f(t)=limtt0f1(t),limtt0f2(t),limtt0f3(t)=L1,L2,L3=L.

Lemma 3.20

Let f(t)=f1(t),f2(t),f3(t) be a GPFF for tIR. If the limit of f(t) exists for all tI and limtt0f(t)=a, where a=a1,a2,a3, then a is PFN.

Proof

Suppose that the limit of f(t) exists for all tI and limtt0f(t)=a. From Definition 3.19, we have limtt0f1(t)=a1, limtt0f2(t)=a2, and limtt0f3(t)=a3. Because f(t) is a GPFF for all tI, we get 0f1(t)+f2(t)+f3(t)1 and 0f1(t)1, 0f2(t)1, 0f3(t)1. Putting g(t)=f1(t)+f2(t)+f3(t), then g(t) is a real-valued function and 0g(t)1 for all tI. Thus, with t0I we have

0limtt0g(t)10limtt0f1(t)+f2(t)+f3(t)10limtt0f1(t)+limtt0f2(t)+limtt0f3(t)10a1+a2+a31

At the same time, since 0f1(t)1, 0f2(t)1, and 0f3(t)1, then 0limtt0f1(t)1, 0limtt0f2(t)1, and 0limtt0f3(t)1 with t0I. Thus, we obtain 0a11, 0a21, and 0a31. Therefore, a=a1,a2,a3 is a PFN.

Theorem 3.21

Let f(t)=f1(t),f2(t),f3(t) be a GPFF with tIR and L=L1,L2,L3 be a PFN. Then, limtt0f(t)=L if and only if for every ε>0 there is a number δ>0 such that if 0|t-t0|δ, then

HDgf(t),Lε.

Proof

Let f(t)=f1(t),f2(t),f3(t) be a GPFF and L=L1,L2,L3 be a PFN.

Necessity: Suppose that limtt0f(t)=L, then limtt0f(t) exists, by Definition 3.19 we get

limtt0f(t)=limtt0f1(t),limtt0f2(t),limtt0f3(t)=L1,L2,L3=L

and limtt0f1(t)=L1, limtt0f2(t)=L2, and limtt0f3(t)=L3. Because these limits of the component functions are limit of real-valued function, by Definition 2.9, for every ε>0 there exists δ1>0, δ2>0, and δ3>0 such that if 0|t-t0|δ1 then |f1(t)-L1|ε, if 0|t-t0|δ2 then |f2(t)-L2|ε, and if 0|t-t0|δ3 then |f3(t)-L3|ε. Putting δ=min{δ1,δ2,δ3}, then if 0|t-t0|δ, we have

HDgf(t),L=supi1,3¯fi-Lisupε,ε,ε=ε.

Thus, for every ε>0 there exists δ>0 such that if 0|t-t0|δ, then HDgf(t),Lε.

Necessity: Suppose that for every ε>0 there exists δ>0 such that if 0|t-t0|δ, then

HDgf(t),L=supi1,3¯fi-Liε.

Thus, if 0|t-t0|δ, then f1-L1ε, f2-L2ε, and f3-L3ε. By Definition 2.9, we have limtt0f1(t)=L1, limtt0f2(t)=L2, and limtt0f3(t)=L3. And, by Definition 3.19,

limtt0f(t)=limtt0f1(t),limtt0f2(t),limtt0f3(t)=L1,L2,L3=L.

Definition 3.22

Let f(n)=f1(n),f2(n),f3(n) be a GPFF with nN and put x(n)=f(n), then we define that a sequence of PFNs, {x(1),x(2),x(3),...}, is denoted by x(n)nN or x(n) for short.

Definition 3.23

Let x(n)Dg be a sequence of PFNs with nN and x(n)=x1(n),x2(n),x3(n). If the limits of component sequences exist, i.e., limn+x1(n)=x1(0), limn+x2(n)=x2(0), and limn+x3(n)=x3(0), then we define the limit of sequence x(n) as follows:

limn+x(n)=limn+x1(n),limn+x2(n),limn+x3(n)=x1(0),x2(0),x3(0)=x(0).

On the other hands, the sequence x(n) is convergent in Dg if there exist x(0)Dg such that limn+x(n)=x(0).

Corollary 3.24

Let x(n) be a sequence of PFNs with nN and x(n)=x1(n),x2(n),x3(n). Then, we have:

  1. If the limit of sequence x(n) exists and limn+x(n)=x(0)=x1(0),x2(0),x3(0), then x(0) is a PFN;

  2. limn+x(n)=x(0) if and only if for every ε>0 there is a number NN such that if n>N, then
    HDgx(n),x(0)ε.

Proof

The item (1) is a direct result of Lemma 3.20 when we replace tI with nN and put f(n)=x(n). For the item (ii), suppose that limn+x(n)=x(0), then the limit of x(n) exists, by Definition 3.23 we obtain limn+x(n)=x(0), where x(0)=x1(0),x2(0),x3(0), and limn+x1(n)=x1(0), limn+x2(n)=x2(0), and limn+x3(n)=x3(0). Because these limits of the component sequences are limit of real numbers sequence, by Definition 2.10, for every ε>0 there exists N1N, N2N, and N3N such that if n>N1 then |x1(n)-x1(0)|ε, if n>N2 then |x2(n)-x2(0)|ε, and if n>N1 then |x3(n)-x3(0)|ε, Putting N=min{N1,N2,N3}, then if n>N, we have

HDgx(n),x(0)=supi1,3¯xi(n)-xi(0)supε,ε,ε=ε.

Thus, for every ε>0 there exists NN such that if n>N, then HDgx(n),x(0)ε.

On the contrary, suppose that for every ε>0 there exists NN such that if n>N, then

HDgx(n),x(0)=supi1,3¯xi(n)-xi(0)ε.

Thus, if n>N, then x1(n)-x1(0)ε, x2(n)-x2(0)ε, and x3(n)-x3(0)ε. By Definition 2.10, we have limn+x1(n)=x1(0), limn+x2(n)=x2(0), and limn+x3(n)=x3(0). And, by Definition 3.23,

limn+x(n)=limn+x1(n),limn+x2(n),limn+x3(n)=x1(0),x2(0),x3(0)=x(0).

Theorem 3.25

Let x(n), y(n), and z(n) be the sequences of PFNs with nN, where x(n)=x1(n),x2(n),x3(n), y(n)=y1(n),y2(n),y3(n), and z(n)=z1(n),z2(n),z3(n), respectively. We have the following properties:

  1. If the limit of x(n) exists, then it is unique;

  2. If x(n)iy(n) with i{1,2,3,4,5,6,7,8} and nN, which N is a fixed positive integer, and limn+x(n)=x(0),limn+y(n)=y(0), then x(0)iy(0).

  3. If x(n)iy(n)iz(n) with i{1,2,3,4,5,6,7,8} and nN, which N is a fixed positive integer, and limn+x(n)=limn+z(n)=L, where L=(L1,L2,L3), then limn+y(n)=L.

Proof

For (i), suppose that x(0),y(0)Dg and x(0)y(0) are two limits of x(n). For every ε>0, since limn+x(n)=x(0), there exists N1N such that if nN1, then HDgx(n),x(0)ε, and limn+x(n)=y(0), there exists N2N such that if nN2, then HDgx(n),y(0)ε. Let ε=12HDgx(0),y(0) and N=max{N1,N2}, then nN, we obtain

HDgx(0),y(0)HDgx(n),x(0)+HDgx(n),y(0)<2ε=HDgx(0),y(0).

This is a contradiction, so x(0)=y(0).

For (2), we first consider the case i=1, suppose that x(n)1y(n) and nN, which N is a fixed positive integer, and limn+x(n)=x(0),limn+y(n)=y(0), where x(0)=x1(0),x2(0),x3(0) and y(0)=y1(0),y2(0),y3(0), respectively. From Definition 3.23, we have

limn+x1(n)=x1(0)limn+x2(n)=x2(0)limn+x3(n)=x3(0)andlimn+y1(n)=y1(0)limn+y2(n)=y2(0)limn+y3(n)=y3(0). 3.4

Because the component sequences xj(n) and yj(n) with j{1,2,3} are sequences in [0,1]R, so they converge as sequence of real numbers. At the same time, x(n)1y(n), i.e., x1(n)y1(n), x2(n)y2(n), and x3(n)y3(n). Therefore, we obtain x1(0)y1(0), x2(0)y2(0), and x3(0)y3(0), this implies that x(0)1y(0). In a similar way, we can prove cases i{2,3,4,5,6,7,8}.

For (iii), let us first consider the case i=1, suppose that x(n)1y(n)1z(n) and nN, which N is a fixed positive integer, and limn+x(n)=limn+z(n)=L, where L=L1,L2,L3. From Definition 3.23, we have

limn+x1(n)=limn+z1(n)=L1limn+x2(n)=limn+z2(n)=L2limn+x3(n)=limn+z3(n)=L3. 3.5

Since the component sequences xj(n), yj(n) and zj(n) with j{1,2,3} are sequences in [0,1]R, so they converge as sequence of real numbers. Besides, x(n)1y(n)1z(n), i.e., x1(n)y1(n)z1(n), x2(n)y2(n)z2(n), and x3(n)y3(n)z3(n). Thus, we obtain limn+y1(n)=L1, limn+y2(n)=L2, and limn+y3(n)=L3. From the item (i) of Corollary 3.24, we obtain LDg and Definition 3.23 we get limn+y(n)=L. In a similar way, we can prove cases i{2,3,4,5,6,7,8}.

Theorem 3.26

Let x(n) and y(n) be the sequences of PFNs with nN that possess limits as n+, where x(n)=x1(n),x2(n),x3(n) and y(n)=y1(n),y2(n),y3(n), respectively. Then,

  1. limn+x(n)gy(n)=limn+x(n)glimn+y(n);

  2. limn+x(n)gy(n)=limn+x(n)glimn+y(n);

  3. limn+λgx(n)=λglimn+x(n);

  4. limn+x(n)gy(n)=limn+x(n)glimn+y(n).

Proof

In each item of this theorem, the basic procedure is to use Definition 3.23 and then, analyze the individual component sequences using the limit properties which have already been used to develop the real-valued functions. For (1.), from the item (i) of Corollary 3.6, we get

limn+x(n)gy(n)=limn+x1(n)+y1(n)21-p,x2(n)+y2(n)21-p,x3(n)+y3(n)21-p=limn+x1(n)+y1(n)21-p,limn+x2(n)+y2(n)21-p,limn+x3(n)+y3(n)21-p=limn+x1(n)+limn+y1(n)21-p,limn+x2(n)+limn+y2(n)21-p,limn+x3(n)+limn+y3(n)21-p=limn+x1(n),limn+x2(n),limn+x3(n)glimn+y1(n),limn+y2(n),limn+y3(n)=limn+x(n)glimn+y(n).

For (2.), from Definition 3.13, we get

  • Case 1 If x(n),y(n)D1 for all nN, then x1(n)y1(n), x2(n)y2(n), and x3(n)y3(n) and
    limn+x(n)gy(n)=limn+y1(n)-x1(n),y2(n)-x2(n),y3(n)-x3(n)=limn+y1(n)-limn+x1(n),limn+y2(n)-limn+x2(n),limn+y3(n)-limn+x3(n)=limn+x1(n),limn+x2(n),limn+x3(n)glimn+y1(n),limn+y2(n),limn+y3(n)=limn+x(n)glimn+y(n).
  • Case 2 If x(n),y(n)D2 for all nN, then y1(n)x1(n), x2(n)y2(n), and x3(n)y3(n) and
    limn+x(n)gy(n)=limn+x1(n)-y1(n),y2(n)-x2(n),y3(n)-x3(n)=limn+x1(n)-limn+y1(n),limn+y2(n)-limn+x2(n),limn+y3(n)-limn+x3(n)=limn+x1(n),limn+x2(n),limn+x3(n)glimn+y1(n),limn+y2(n),limn+y3(n)=limn+x(n)glimn+y(n).
  • Case 3, 4, 5, 6, 7, 8 With x(n),y(n)D3, x(n),y(n)D4, x(n),y(n)D5, x(n),y(n)D6, x(n),y(n)D7, and x(n),y(n)D8, respectively. We also demonstrate a similar way.

For (3.), from Definition 3.8, we get

limn+λgx(n)=limn+λx1(n),λx2(n),λx3(n)=limn+λx1(n),limn+λx2(n),limn+λx3(n)=λlimn+x1(n),λlimn+x2(n),λlimn+x3(n)=λglimn+x1(n),limn+x2(n),limn+x3(n)=λglimn+x(n).

For (4.), from Definition 3.17, we get

limn+x(n)gy(n)=limn+x1(n)y1(n),x2(n)y2(n),x3(n)y3(n)=limn+x1(n)y1(n),limn+x2(n)y2(n),limn+x3(n)y3(n)=limn+x1(n).limn+y1(n),limn+x2(n).limn+y2(n),limn+x3(n).limn+y3(n)=limn+x1(n),limn+x2(n),limn+x3(n)glimn+y1(n),limn+y2(n),limn+y3(n)=limn+x(n)glimn+y(n).

In classical mathematics, the completeness of a metric space is an important property because if this space is incomplete, then the limit operation will be meaningless and it is not entirely well-behaved metric space. Therefore, we will demonstrate that the metric semi-linear space of PFNs is complete in the following.

Definition 3.27

Let x(n) be a sequence of PFNs with nN. In the metric space Dg,HDg, x(n) is a Cauchy sequence if for every ε>0 there is a number NN such that if m,n>N, then

HDgx(m),x(n)<ε.

Theorem 3.28

Dg,HDg is a complete metric space.

Proof

Suppose that x(n)nN is a Cauchy sequence of PFNs in Dg,HDg, where x(n)=x1(n),x2(n),x3(n). Since the component sequences xi(n) with i{1,2,3} are sequences in [0,1]R under the absolute-value metric, so we have |xi(m)-xi(n)|HDgx(m),x(n) m,n1. Besides, x(n) is a Cauchy sequence, i.e, for every ε>0 there is a number NN such that if m,n>N, then

|xi(m)-xi(n)|HDgx(m),x(n)<ε.

Thus, xi(n) with i{1,2,3} are Cauchy sequences in [0,1]R that [0, 1] is a complete metric space under the absolute-value metric. So the component sequences xi(n) with i{1,2,3} are convergent in [0, 1],  i.e, there exist xi(0)Dg such that limn+xi(n)=xi(0). Therefore, by Definition 3.23 we obtain limn+x(n)=limn+x1(n),limn+x2(n),limn+x3(n)=x1(0),x2(0),x3(0). At the same time, by the item (i) of Corollary 3.24, we get x1(0),x2(0),x3(0)Dg. So Dg,HDg is a complete metric space.

Remark 3.29

For the picture fuzzy set, we have a realistic illustration of the voting problem, which is given by Cuong in (Cuong 2014). His idea was to divide the voters into four groups (including voting in favor, abstaining from voting, voting against, refusal of the voting). For the picture fuzzy number which is the main object in this study, we only need to consider three groups of subjects participating in voting, namely voting for, abstaining, and voting against. To relate the results of this study to real-life such as a matter of voting for something, for example, a new law, a new administrator, a certain choice, and so on. We concretize this problem as follows: Given A and B are two places holding the vote about something in a certain area X, to make this easier to visualize, we assume that A and B are two provinces of country X and these two provinces are organizing people vote to pass or reject a new regulation. Now, for the space Dg, without loss of generality, we consider A and B to be two picture fuzzy numbers with A=(x1,x2,x3) and B=(y1,y2,y3). Where, x1, x2, x3 and y1, y2, y3 are the ratio of the number of votes of the three groups: vote for, abstain and vote against of A and B compared to the population of each province, respectively. From the results in this study, the following can be inferred:

Firstly, the sum between A and B in the space Dg exists. If people in two provinces A and B both participate in the vote, then from Definition 3.5, this means that m=2, p0 and the sum of A and B in the space Dg is A+B=x1+y12,x2+y22,x3+y32, means the sum of the proportions of the votes of the population that voted in favor of provinces A and B to the population of each province is x1+y12, similarly for the total ratio of abstaining and voting against of A and B. If province A organizes for people to vote and province B does not, that is, B is the zero element in the space Dg, then we have m=2 and p=1. Therefore, A+B=A, this result is true because province B does not organize people to vote.

Secondly, multiplying a scalar value kR+ by an element A in the space Dg, will show the impact of objective or subjective factors on the value of the element A. For example, in voting, province A decided not to hold direct voting due to the appearance of the Covid-19 epidemic. This means that the value of the three groups: voting for, abstaining, and voting against of province A in the space Dg is A=(0,0,0). in the space Dg, we can explain this as follows: suppose that the values of the three groups: voting for, abstaining, voting against of province A is A=(x1,x2,x3), if province A allows people to vote, then obviously A0. However, with k=0 representing the Covid-19 epidemic appears, then we will now have kA=(kx1,kx2,kx3)=(0x1,0.x2,0.x3)=(0,0,0). Hence, the result of the multiplication scalar kA reflects the fact that the voting results of the three groups: voting for, abstain, and vote against in province A is (0, 0, 0). because this province did not vote.

Finally, from a mathematical point of view, the limit is the value a function approaches when the input variable approaches a certain value. Corresponding to the voting problem, the limit of the voting process will give us a result that the sum of the proportions of the votes of the three groups voting for, abstaining, and voting against compared to the number of voters will not be more than 1. This is true because if the result of the election process is that the sum of votes of the three groups voting for, abstaining, and voting against is greater than 1, then it is clear that this voting process has fraud on the number of votes.

Conclusions

In this paper, we establish the concept of the limit and study its properties on the metric semi-linear space of PFNs. Firstly, we propose two new operations, addition and scalar multiplication, to replace two of Wei’s operations. We also discuss some limitations of Wei’s two operations. These are also the reason that we want to replace them with two new operations with more advantages. The highlight is that the set of PFNs together with these two new operations becomes a semi-linear space. Along with that we also provide some related concepts on this semi-linear space such as metric, order relations between two PFNs, geometric difference, multiplication of two PFNs. Next, we define a type of function whose value is in this semi-linear space. It called the geometry picture fuzzy function and used to give the concept of limit for it and the sequences of PFNs in the metric semi-linear space of PFNs. Finally, to ensure that the limit operations are well-defined over the metric semi-linear space of PFNs, we proved that this space is complete.

Acknowledgements

The authors are very grateful to the anonymous reviewer and associate editor for their insightful and constructive suggestions that have led to an improved version of this manuscript.

Author Contributions

The authors declare that the study was realized in collaboration with equal responsibility. All authors read and approved the final manuscript.

Funding

The authors have not disclosed any funding.

Data availability

All data generated or analyzed during this study are included in this article.

Declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

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Contributor Information

Nguyen Dinh Phu, Email: ndphu@qtu.edu.vn.

Nguyen Nhut Hung, Email: nguyennhuthung@hcmuaf.edu.vn.

Ali Ahmadian, Email: ahmadian.hosseini@gmail.com.

Soheil Salahshour, Email: soheisalahshour@yahoo.com.

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