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. 2022 Nov 5;117(1):14. doi: 10.1007/s13398-022-01343-0

Richards’s curve induced Banach space valued ordinary and fractional neural network approximation

George A Anastassiou 1,, Seda Karateke 2
PMCID: PMC9638447  PMID: 36373128

Abstract

Here we perform the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives. Our operators are defined by using a density function generated by the Richards curve, which is generalized logistic function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.

Keywords: Richards curve function, Banach space valued neural network approximation, Banach space valued quasi-interpolation operator, Modulus of continuity, Banach space valued Caputo fractional derivative, Banach space valued fractional approximation

Introduction

The first author in [1, 2], see Chapters 2–5, was the first to establish neural network approximation to continuous functions with rates by very specifically defined neural network operators of Cardaliagnet–Euvrard and “Squashing” types, by employing the modulus of continuity of the engaged function or its high order derivative, and producing very tight Jackson type inequalities. He treats there both the univariate and multivariate cases. The defining these operators “bell-shaped ”and “squashing ”functions are assumed to be of compact suport. Also in [2] he gives the Nth order asymptotic expansion for the error of weak approximation of these two operators to a special natural class of smooth functions, see Chapters 4–5 there.

Again the first author inspired by [14], continued his studies on neural networks approximation by introducing and using the proper quasi-interpolation operators of sigmoidal and hyperbolic tangent type which resulted into [37], by treating both the univariate and multivariate cases. He did also the corresponding fractional cases [8, 9, 13].

The authors here perform Richards’s curve activated neural network approximations to continuous functions over compact intervals of the real line or over the whole R with valued to an arbitrary Banach space X,·. Finally they treat completely the related X-valued fractional approximation. All convergences here are with rates expressed via the modulus of continuity of the involved function or its X-valued high order derivative, or X-valued fractional derivatives and given by very tight Jackson type inequalities.

Our compact intervals are not necessarily symmetric to the origin. Some of our upper bounds to error quantity are very flexible and general. In preparation to prove our results we establish important properties of the basic density function defining our operators which is induced by the Richards curve, which is a sigmoid function. Richards’s curve among others has been used for modeling COVID-19 infection trajectory [17].

Feed-forward X-valued neural networks (FNNs) with one hidden layer, the only type of networks we deal with in this article, are mathematically expressed as

Nnx==0ncjσaj·x+bj,xRs,sN,

where for 0jn, bjR are the thresholds, ajRs are the connection weights, cjX are the coefficients, aj·x is the inner product of aj and x, and σ is the activation function of the network. About neural networks in general read [15, 18, 20].

Preliminaries

A Richards’s curve is

φx=11+e-μx;xR,μ>0, 1

which is strictly increasing on R, and it is a sigmoid function, in particular this is a generalized logistic function [21].

See that

limx+φx=1andlimx-φx=0. 2

We consider the following activation function

Gx=12φx+1-φx-1,xR, 3

which is G(x)>0, all xR.

The function φ has great applications in epidemiology and especially in COVID-19 modeling infection trajectories [17].

We have that

φ0=12andφx=1-φ-x. 4

We notice that

G-x:=12φ-x+1-φ-x-1=121-φx-1-1+φx+1
=12φx+1-φx-1=Gx,xR. 5

So that G is an even function.

We have that

G0=12φ1-φ-1=121-φ-1-φ-1=121-2φ-1=121-21+eμ=121+eμ-21+eμ=12eμ-1eμ+1,

that is

G0=eμ-12eμ+1. 6

Let x0, we have that

Gx=12φx+1-φx-1=121+e-μx+1-1-1+e-μx-1-1=12-11+e-μx+1-2e-μx+1-μ--11+e-μx-1-2e-μx-1-μ=12μe-μx+11+e-μx+1-2-μe-μx-11+e-μx-1-2=μ2e-μx+11+e-μx+12-e-μx-11+e-μx-12=μ21eμx+11+e-2μx+1+2e-μx+1-1eμx-11+e-2μx-1+2e-μx-1=μ21eμx+1+e-μx+1+2-1eμx-1+e-μx-1+2=μ41eμx+1+e-μx+12+1-1eμx-1+e-μx-12+1=μ41cosμx+1+1-1cosμx-1+1=μ4cosμx-1-cosμx+1cosμx+1+1cosμx-1+1<0,x1. 7

So for x1, G(x)<0 and G(x) is strictly decreasing.

Let now 0<x<1, then 1-x>0 and 0<1-x<1+x, then coshμ(x-1)=coshμ(1-x)<coshμ(x+1), so that again G(x)<0, and G(x) is strictly decreasing over 0<x<1.

Thus G(x) is strictly decreasing on 0,+.

Clearly, G(x) is strictly increasing on -,0, and G(0)=0.

We observe that

limx+Gx=12φ+-φ+=0,limx-Gx=12φ--φ-=0. 8

That is, the x-axis is the horizontal asymptote for G.

Conclusion, G is a bell symmetric function with maximum

G0=eμ-12(eμ+1). 9

We need

Theorem 1

It holds

i=-Gx-i=1,xR. 10

Proof

We observe that

i=-φx-i-φx-1-i=i=0φx-i-φx-1-i+i=--1φx-i-φx-1-i.

Furthermore (λZ+)

i=0φx-i-φx-1-i 11
limλi=0λφx-i-φx-1-i(telescoping sum)=limλφx-φx-λ+1=φx.

Similarly, it holds

i=--1φx-i-φx-1-i=limλi=-λ-1φx-i-φx-1-i=limλφx+λ-φx=1-φx. 12

Therefore we derive

i=-φx-i-φx-1-i=1,xR, 13

and

i=-φx+1-i-φx-i=1,xR. 14

Adding the last two equations we get

i=-φx+1-i-φx-1-i=2,xR. 15

Since

Gx=12φx+1-φx-1,

we have that

Gx-i=12φx+1-i-φx-1-i, 16

giving i=-Gx-i=1.

Remark 2

Because G is even it holds

i=-Gi-x=1,xR.

Hence

i=-Gi+x=1,xR,

and

i=-Gx+i=1,xR. 17

Theorem 3

It holds

-Gxdx=1. 18

Proof

We observe that

-Gxdx=j=-jj+1Gxdx=j=-01Gx+jdx=01j=-Gx+jdx=011dx=1.

So Gx is a density function.

We need

Theorem 4

Let 0<α<1, and nN with n1-α>2. It holds

k=-:nx-kn1-αGnx-k<1eμn1-α-2,μ>0. 19

Proof

We have that

Gx=12φx+1-φx-1,xR.

Let x1. That is 0x-1<x+1. Applying the mean value theorem we get

Gx=12·2·φξ=φ(ξ)=μe-μξ1+e-μξ2,

where 0x-1<ξ<x+1.

Notice that

G(x)<μe-μξ<μe-μ(x-1),x1.

Thus, we have

k=-:nx-kn1-αGnx-k=k=-:nx-kn1-αGnx-kμk=-:nx-kn1-αe-μnx-k-1
μn1-α-1e-μ(x-1)dx=μn1-α-2e-μzdz 20
=n1-α-2e-μzd(μz)=n1-α-2e-ydy=-e-yn1-α-2
=e-μzn1-α-2=e-μ(n1-α-2)=1eμ(n1-α-2), 21

for n1-α>2, nN. We have found that

k=-:nx-kn1-αGnx-k<1eμ(n1-α-2), 22

for n1-α>2, nN.

Denote by · the integral part of the number and by · the ceiling of the number.

Theorem 5

Let a,bR and nN so that nanb. It holds

1k=nanbGnx-k<4(1+e-2μ)1-e-2μ,μ>0, 23

xa,b.

Proof

Let xa,b. We see that

1=k=-Gnx-k>k=nanbGnx-k
=k=nanbGnx-k>Gnx-k0, 24

k0na,nbZ.

We can choose k0na,nbZ such that nx-k0<1.

Therefore we get that

Gnx-k0>G1=12φ2-12=1211+e-2μ-12=1-e-2μ4(1+e-2μ), 25

and

k=nanbGnx-k>1-e-2μ4(1+e-2μ). 26

That is

1k=nanbGnx-k<4(1+e-2μ)1-e-2μ, 27

proving the claim.

We make

Remark 6

We also notice that

1-k=nanbGnb-k=k=-na-1Gnb-k+k=nb+1Gnb-k
>Gnb-nb-1 28

(call ε:=nb-nb, 0ε<1)

=Gε-1=G1-εG1>0.

Therefore

limn1-k=nanbGnb-k>0. 29

Similarly,

1-k=nanbGna-k=k=-na-1Gna-k+k=nb+1Gna-k>Gna-na+1

(call η:=na-na, 0η<1)

=G1-ηG1>0. 30

Therefore again

limn1-k=nanbGna-k>0. 31

Here we find that

limnk=nanbGnx-k1,\ for at least somexa,b. 32

Note 7

Let a,bR. For large enough n we always obtain nanb. Also aknb, iff naknb.

In general it holds (by i=-Gx-i=1, xR) that is

k=nanbGnx-k1. 33

Let X,· be a Banach space.

Definition 8

Let fCa,b,X and nN:nanb. We introduce and define the X-valued linear neural network operators

Lnf,x:=k=nanbfknGnx-kk=nanbGnx-k,xa,b. 34

Clearly here Lnf,xCa,b,X.

For convenience we use the same Ln for real valued functions when needed. We study here the pointwise and uniform convergence of Lnf,x to fx with rates.

For convenience, also we call

Lnf,x:=k=nanbfknGnx-k, 35

(similarly, Ln can be defined for real valued functions) that is

Lnf,x:=Lnf,xk=nanbGnx-k. 36

So that

Lnf,x-fx=Lnf,xk=nanbGnx-k-fx
=Lnf,x-fxk=nanbGnx-kk=nanbGnx-k. 37

Consequently, we derive that

Lnf,x-fx4(1+e-2μ)1-e-2μLnf,x-fxk=nanbGnx-k
=4(1+e-2μ)1-e-2μk=nanbfkn-fxGnx-k. 38

We will estimate the right hand side of the last quantity.

For that we need, for fCa,b,X the first modulus of continuity

ω1f,δ:=supx,ya,bx-yδfx-fy,δ>0. 39

Similarly, it is defined ω1 for fCuBR,X (uniformly continuous and bounded functions from R into X), for fCBR,X (continuous and bounded X-valued), and for fCuR,X (uniformly continuous).

The fact fCa,b,X or fCuR,X, is equivalent to limδ0ω1f,δ=0, see [11].

We make

Definition 9

When fCuBR,X, or fCBR,X, we define

Ln¯f,x:=k=-fknGnx-k, 40

nN, xR, the X-valued quasi-interpolation neural network operator.

We make

Remark 10

We have that

fknf,R<+,

and

fknGnx-kf,RGnx-k 41

and

k=-λλfknGnx-kf,Rk=-λλGnx-k,

and finally

k=-fknGnx-kf,R, 42

a convergent series in R.

So, the series k=-fknGnx-k is absolutely convergent in X, hence it is convergent in X and Ln¯f,xX. We denote by f:=supxa,bfx, for fCa,b,X, similarly it is defined for fCBR,X.

Main results

We present a set of X-valued neural network approximations to a function given with rates.

Theorem 11

Let fCa,b,X, μ>0, 0<α<1, nN:n1-α>2, xa,b. Then

(i)

graphic file with name 13398_2022_1343_Equ43_HTML.gif 43

and

(ii)

Lnf-fρ. 44

We get that limnLnf=f, pointwise and uniformly.

Proof

We see that

k=nanbfkn-fxGnx-kk=nanbfkn-fxGnx-k=k=nakn-x1nαnbfkn-fxGnx-k
+k=nakn-x>1nαnbfkn-fxGnx-k 45
k=nakn-x1nαnbω1f,kn-xGnx-k+2fk=nak-nx>n1-αnbGnx-kω1f,1nαk=-kn-x1nαGnx-k+2fk=-k-nx>n1-αGnx-k(by Theorem 4)ω1f,1nα+2feμ(n1-α-2). 46

That is

k=nanbfkn-fxGnx-kω1f,1nα+2feμ(n1-α-2). 47

Using the last equality we derive (43).

Next we give

Theorem 12

Let fCBR,X, 0<α<1, μ>0, nN:n1-α>2, xR. Then

  • (i)
    L¯nf,x-fxω1f,1nα+2feμ(n1-α-2)=:γ, 48
    and
  • (ii)
    L¯nf-fγ. 49
    For fCuBR,X we get limnL¯nf=f, pointwise and uniformly.

Proof

We observe that

L¯nf,x-fx=k=-fknGnx-k-fxk=-Gnx-k=k=-fkn-fxGnx-kk=-fkn-fxGnx-k=k=-kn-x1nαfkn-fxGnx-k+k=-kn-x>1nαfkn-fxGnx-k 50
k=-kn-x1nαω1f,kn-xGnx-k+2fk=-kn-x>1nαGnx-kω1f,1nαk=-kn-x1nαGnx-k+2feμ(n1-α-2)ω1f,1nα+2feμ(n1-α-2), 51

proving the claim.

We need the X-valued Taylor’s formula in an appropiate form:

Theorem 13

[10, 12] Let NN, and fCNa,b,X, where a,bR and X is a Banach space. Let any x,ya,b. Then

fx=i=0Nx-yii!fiy+1N-1!yxx-tN-1fNt-fNydt. 52

The derivatives fi, iN, are defined like the numerical ones, see [22], p. 83. The integral yx in (52) is of Bochner type, see [19].

By [12, 16] we have that: if fCa,b,X, then fLa,b,X and fL1a,b,X.

In the next we discuss high order neural network X-valued approximation by using the smoothness of f.

Theorem 14

Let fCNa,b,X, n,NN, μ>0, 0<α<1, xa,b and n1-α>2. Then

  • (i)
    graphic file with name 13398_2022_1343_Equ53_HTML.gif 53
  • (ii)
    assume further fjx0=0, j=1,...,N, for some x0a,b, it holds
    Lnf,x0-fx04(1+e-2μ)1-e-2μ·×ω1fN,1nα1nαNN!+2fNb-aNN!eμn1-α-2, 54
    and
  • (iii)
    Lnf-f4(1+e-2μ)1-e-2μj=1Nfjj!1nαj+b-ajeμn1-α-2+ω1fN,1nα1nαNN!+2fNb-aNN!eμn1-α-2. 55
    Again we obtain limnLnf=f, pointwise and uniformly.

Proof

Next we apply the X-valued Taylor’s formula with Bochner integral remainder (52). We have (here kn,xa,b)

fkn=j=0Nfjxj!kn-xj+xknfNt-fNxkn-tN-1N-1!dt. 56

Then

fknGnx-k=j=0Nfjxj!Gnx-kkn-xj+Gnx-kxknfNt-fNxkn-tN-1N-1!dt. 57

Hence

k=nanbfknGnx-k-fxk=nanbGnx-k=j=0Nfjxj!k=nanbGnx-kkn-xj+k=nanbGnx-kxknfNt-fNxkn-tN-1N-1!dt. 58

Thus

Lnf,x-fxk=nanbGnx-k=j=1Nfjxj!Ln·-xj+Λnx, 59

where

Λnx:=k=nanbGnx-kxknfNt-fNxkn-tN-1N-1!dt. 60

We assume that b-a>1nα, which is always the case for large enough nN, that is when n>b-a-1n.

Thus kn-x1nα or kn-x>1nα.

Let

ψ:=xknfNt-fNxkn-tN-1N-1!dt, 61

in the case of kn-x1nα, we find that

ψω1fN,1nα1nαNN! 62

for xkn or xkn.

We prove it next.

  • (i)
    Indeed, for the case of xkn, we have
    ψ=xknfNt-fNxkn-tN-1N-1!dtxknfNt-fNxkn-tN-1N-1!dtxknω1fN,t-xkn-tN-1N-1!dtω1fN,1nαxknkn-tN-1N-1!dt=ω1fN,1nαkn-xNN!ω1fN,1nα1nαNN!. 63
  • (ii)
    for the case of x>kn, we have
    ψ=xknfNt-fNxkn-tN-1N-1!dt=knxfNt-fNxt-knN-1N-1!dtknxfNt-fNxt-knN-1N-1!dtknxω1fN,t-xt-knN-1N-1!dtω1fN,1nαknxt-knN-1N-1!dt=ω1fN,1nαx-knNN!ω1fN,1nα1nαNN!. 64
    We have proved (62).

We treat again ψ, see (61), but differently:

Notice also for xkn that

xknfNt-fNxkn-tN-1N-1!dtxknfNt-fNxkn-tN-1N-1!dt2fNxknkn-tN-1N-1!dt=2fNkn-xNN!2fNb-aNN!. 65

Next assume knx, then

xknfNt-fNxkn-tN-1N-1!dt=knxfNt-fNxt-knN-1N-1!dtknxfNt-fNxt-knN-1N-1!dt 66
2fNknxt-knN-1N-1!dt=2fNx-knNN!2fNb-aNN!.

Thus

ψ2fNb-aNN!. 67

in the two cases.

Therefore

Λnx=k=nakn-x1nαnbGnx-kψ+k=nakn-x>1nαnbGnx-kψ. 68

Hence

Λnxk=nakn-x1nαnbGnx-kω1fN,1nα1N!nαN 69
+k=nakn-x>1nαnbGnx-k2fNb-aNN!ω1fN,1nα1N!nαN+1eμn1-α-22fNb-aNN!=

That is

Λnxω1fN,1nαN!nαN+2fNb-aNN!eμn1-α-2, 70

xa,b.

We further see that

Ln·-xj=k=nanbGnx-kkn-xj, 71

where Ln is defined similarly for real valued functions.

Therefore

Ln·-xjk=nanbGnx-kkn-xj=k=nakn-x1nαnbGnx-kkn-xj+k=nakn-x>1nαnbGnx-kkn-xj(23)1nαj+b-aj1eμn1-α-2. 72

That is

Ln·-xj1nαj+b-aj1eμn1-α-2, 73

for j=1,...,N.

Putting things together we have proved

Lnf,x-fxk=nanbGnx-kj=1Nfjxj!1nαj+b-ajeμn1-α-2+ω1fN,1nα1nαNN!+2fNb-aNN!eμn1-α-2, 74

that is establishing the theorem.

All integrals from now on are of Bochner type [19].

We need

Definition 15

[12] Let a,bR, X be a Banach space, α>0; m=αN, (· is the ceiling of the number), f:a,bX. We assume that fmL1a,b,X. We call the Caputo–Bochner left fractional derivative of order α:

Daαfx:=1Γm-αaxx-tm-α-1fmtdt,xa,b. 75

If αN, we set Daαf:=fm the ordinary X-valued derivative (defined similar to numerical one, see [22], p. 83), and also set Da0f:=f.

By [12], Daαfx exists almost everywhere in xa,b and DaαfL1a,b,X.

If fmLa,b,X<, then by [12], DaαfCa,b,X, hence DaαfCa,b.

We mention

Lemma 16

[11] Let α>0, αN, m=α, fCm-1a,b,X and fmLa,b,X. Then Daαfa=0.

We mention

Definition 17

[10] Let a,bR, X be a Banach space, α>0, m:=α. We assume that fmL1a,b,X, where f:a,bX. We call the Caputo–Bochner right fractional derivative of order α:

Db-αfx:=-1mΓm-αxbz-xm-α-1fmzdz,xa,b. 76

We observe that Db-mfx=-1mfmx, for mN, and Db-0fx=fx.

By [10], Db-αfx exists almost everywhere on a,b and Db-αfL1a,b,X.

If fmLa,b,X<, and αN, by [10], Db-αfCa,b,X, hence Db-αfCa,b.

We need

Lemma 18

([11]) Let fCm-1a,b,X, fmLa,b,X, m=α, α>0, αN. Then Db-αfb=0.

We mention the left fractional Taylor formula

Theorem 19

[12] Let mN and fCma,b,X, where a,bR and X is a Banach space, and let α>0:m=α. Then

fx=i=0m-1x-aii!fia+1Γαaxx-zα-1Daαfzdz, 77

xa,b.

We also mention the right fractional Taylor formula

Theorem 20

[10] Let a,bR, X be a Banach space, α>0, m=α, fCma,b,X. Then

fx=i=0m-1x-bii!fib+1Γαxbz-xα-1Db-αfzdz, 78

xa,b.

Convention 21

We assume that

Dx0αfx=0, forx<x0, 79

and

Dx0-αfx=0, forx>x0, 80

for all x,x0a,b.

We mention

Proposition 22

[11] Let fCna,b,X, n=ν, ν>0. Then Daνfx is continuous in xa,b.

Proposition 23

[11] Let fCma,b,X, m=α, α>0. Then Db-νfx is continuous in xa,b.

We also mention

Proposition 24

[11] Let fCm-1a,b,X, fmLa,b,X, m=α, α>0 and

Dx0αfx=1Γm-αx0xx-tm-α-1fmtdt, 81

for all x,x0a,b:xx0.

Then Dx0αfx is continuous in x0.

Proposition 25

[11] Let fCm-1a,b,X, fmLa,b,X, m=α, α>0 and

Dx0-αfx=-1mΓm-αxx0ζ-xm-α-1fmζdζ, 82

for all x,x0a,b:x0x.

Then Dx0-αfx is continuous in x0.

Corollary 26

[11] Let fCma,b,X, m=α, α>0, x,x0a,b. Then Dx0afx, Dx0-afx are jointly continuous functions in x,x0 from a,b2 into X, X is a Banach space.

We need

Theorem 27

[11] Let f:a,b2X be jointly continuous, X is a Banach space. Consider

Gx=ω1f·,x,δ,x,b, 83

δ>0, xa,b.

Then G is continuous on a,b.

Theorem 28

[11] Let f:a,b2X be jointly continuous, X is a Banach space. Then

Hx=ω1f·,x,δ,a,x, 84

xa,b, is continuous in xa,b, δ>0.

We make

Remark 29

[11] Let fCn-1a,b, fnLa,b, n=ν, ν>0, νN. Then

DaνfxfnLa,b,XΓn-ν+1x-an-ν,xa,b. 85

Thus we observe

ω1Daνf,δ=supx,ya,bx-yδDaνfx-Daνfysupx,ya,bx-yδfnLa,b,XΓn-ν+1x-an-ν+fnLa,b,XΓn-ν+1y-an-ν2fnLa,b,XΓn-ν+1b-an-ν. 86

Consequently

ω1Daνf,δ2fnLa,b,XΓn-ν+1b-an-ν. 87

Similarly, let fCm-1a,b, fmLa,b, m=α, α>0, αN, then

ω1Db-αf,δ2fmLa,b,XΓm-α+1b-am-α. 88

So for fCm-1a,b, fmLa,b, m=α, α>0, αN, we find

supx0a,bω1Dx0αf,δx0,b2fmLa,b,XΓm-α+1b-am-α, 89

and

supx0a,bω1Dx0-αf,δa,x02fmLa,b,XΓm-α+1b-am-α. 90

By [12] we get that Dx0αfCx0,b,X, and by [10] we obtain that Dx0-αfCa,x0,X.

We present the following X-valued fractional approximation result by neural networks.

Theorem 30

Let α,μ>0, N=α, αN, fCNa,b,X, 0<β<1, xa,b, nN:n1-β>2. Then

  • (i)
    graphic file with name 13398_2022_1343_Equ185_HTML.gif
    graphic file with name 13398_2022_1343_Equ91_HTML.gif 91
  • (ii)
    if fjx=0, for j=1,...,N-1, we have
    graphic file with name 13398_2022_1343_Equ92_HTML.gif 92
  • (iii)
    graphic file with name 13398_2022_1343_Equ93_HTML.gif 93
    xa,b, and
  • (iv)
    graphic file with name 13398_2022_1343_Equ94_HTML.gif 94
    Above, when N=1 the sum j=1N-1·=0.

As we see here we obtain X-valued fractionally type pointwise and uniform convergence with rates of LnI the unit operator, as n.

Proof

Let xa,b. We have that Dx-αfx=Dxαfx=0.

From Theorem 19, we get by the left Caputo fractional Taylor formula that

fkn=j=0N-1fjxj!kn-xj+1Γαxknkn-Jα-1DxαfJ-DxαfxdJ, 95

for all xknb.

Also from Theorem 20, using the right Caputo fractional Taylor formula we get

fkn=j=0N-1fjxj!kn-xj+1ΓαknxJ-knα-1Dx-αfJ-Dx-αfxdJ, 96

for all aknx.

Hence we have

fknGnx-k=j=0N-1fjxj!Gnx-kkn-xj+Gnx-kΓαxknkn-Jα-1DxαfJ-DxαfxdJ, 97

for all xknb, iff nxknb, and

fknGnx-k=j=0N-1fjxj!Gnx-kkn-xj+Gnx-kΓαknxJ-knα-1Dx-αfJ-Dx-αfxdJ, 98

for all aknx, iff naknx.

Therefore it holds

k=nx+1nbfknGnx-k=j=0N-1fjxj!k=nx+1nbGnx-kkn-xj+1Γαk=nx+1nbGnx-kxknkn-Jα-1×DxαfJ-DxαfxdJ, 99

and

k=nanxfknGnx-k=j=0N-1fjxj!k=nanxGnx-kkn-xj+1Γαk=nanxGnx-kknxJ-knα-1×Dx-αfJ-Dx-αfxdJ. 100

Adding the last two equalities obtain

Lnf,x=k=nanbfknGnx-k=j=0N-1fjxj!k=nanbGnx-kkn-xj+1Γαk=nanxGnx-kknxJ-knα-1Dx-αfJ-Dx-αfxdJ+k=nx+1nbGnx-kxknkn-Jα-1DxαfJ-DxαfxdJ. 101

So we have derived

Lnf,x-fxk=nanbGnx-k=j=1N-1fjxj!Gn·-xj+enx, 102

where

enx:=1Γαk=nanxGnx-kknxJ-knα-1Dx-αfJ-Dx-αfxdJ+k=nx+1nbGnx-kxknkn-Jα-1DxαfJ-DxαfxdJ. 103

We set

e1nx:=1Γαk=nanxGnx-kknxJ-knα-1Dx-αfJ-Dx-αfxdJ, 104

and

e2n:=1Γαk=nx+1nbGnx-kxknkn-Jα-1DxαfJ-DxαfxdJ, 105

i.e.

enx=e1nx+e2nx. 106

We assume b-a>1nβ, 0<β<1, which is always the case for large enough nN, that is when n>b-a-1β. It is always true that either kn-x1nβ or kn-x>1nβ.

For k=na,...,nx, we consider

θ1k:=knxJ-knα-1Dx-αfJ-Dx-αfxdJ=knxJ-knα-1Dx-αfJdJknxJ-knα-1Dx-αfJdJ 107
Dx-αfJ,a,xx-knααDx-αf,a,xx-aαα. 108

That is

θ1kDx-αf,a,xx-aαα, 109

for k=na,...,nx.

Also we have in case of kn-x1nβ that

θ1kknxJ-knα-1Dx-αfJ-Dx-αfxdJknxJ-knα-1ω1Dx-αf,J-xa,xdJ
ω1Dx-αf,x-kna,xknxJ-knα-1dJω1Dx-αf,1nβa,xx-knααω1Dx-αf,1nβa,x1αnαβ. 110

That is when kn-x1nβ, then

θ1kω1Dx-αf,1nβa,xαnαβ. 111

Consequently we obtain

e1nx1Γαk=nanxGnx-kθ1k=1Γαk=na:kn-x1nβnxGnx-kθ1k+k=na:kn-x>1nβnxGnx-kθ1k1Γαk=na:kn-x1nβnxGnx-kω1Dx-αf,1nβa,xαnαβ 112
+k=na:kn-x>1nβnxGnx-kDx-αf,a,xx-aαα
1Γα+1ω1Dx-αf,1nβa,xnαβ+k=-:nx-k>n1-βGnx-kDx-αf,a,xx-aα1Γα+1ω1Dx-αf,1nβa,xnαβ+Dx-αf,a,xx-aαeμn1-β-2. 113

So we have proved that

e1nx1Γα+1ω1Dx-αf,1nβa,xnαβ
+1eμn1-β-2Dx-αf,a,xx-aα. 114

Next when k=nx+1,...,nb we consider

θ2k:=xknkn-Jα-1DxαfJ-DxαfxdJxknkn-Jα-1DxαfJ-DxαfxdJ=xknkn-Jα-1DxαfJdJ 115
Dxαf,x,bkn-xααDxαf,x,bb-xαα. 116

Therefore when k=nx+1,...,nb we get that

That is

θ2kDxαf,x,bb-xαα. 117

In case of kn-x1nβ we have

θ2kxknkn-Jα-1ω1Dxαf,J-xx,bdJω1Dxαf,kn-xx,bxknkn-Jα-1dJω1Dxαf,1nβx,bkn-xααω1Dxαf,1nβx,b1αnαβ. 118

So when kn-x1nβ we derived that

θ2kω1Dxαf,1nβx,bαnαβ. 119

Similarly we have that

e2nx1Γαk=nx+1nbGnx-kγ2k=1Γαk=nx+1:kn-x1nβnbGnx-kθ2k+k=nx+1:kn-x>1nβnbGnx-kθ2k 120
1Γαk=nx+1:kn-x1nβnbGnx-kω1Dxαf,1nβx,bαnαβ+k=nx+1:kn-x>1nβnbGnx-kDxαf,x,bb-xαα1Γα+1ω1Dxαf,1nβx,bnαβ+k=-:kn-x>1nβGnx-kDxαf,x,bb-xα 121
1Γα+1ω1Dxαf,1nβx,bnαβ+1eμn1-β-2Dxαf,x,bb-xα.

So we have proved that

e2nx1Γα+1ω1Dxαf,1nβx,bnαβ+1eμn1-β-2Dxαf,x,bb-xα. 122

Therefore

enxe1nx+e2nx1Γα+1ω1Dx-αf,1nβa,x+ω1Dxαf,1nβx,bnαβ+1eμn1-β-2Dx-αf,a,xx-aα+Dxαf,x,bb-xα. 123

From the proof of Theorem 14 we get that

Ln·-xjx1nβj+b-aj1eμn1-β-2, 124

for j=1,...,N-1, xa,b.

Putting things together, we have established

Lnf,x-fxk=nanbGnx-kj=1N-1fjxj! 125
1nβj+b-aj1eμn1-β-2+1Γα+1ω1Dx-αf,1nβa,x+ω1Dxαf,1nβx,bnαβ
+1eμn1-β-2(Dx-αf,a,xx-aα+Dxαf,x,bb-xα)=:Knx. 126

As a result we derive

Lnf,x-fx4(1+e-2μ)1-e-2μKnx,xa,b. 127

We further have that

Knj=1N-1fjj!1nβj+b-aj1eμn1-β-2
+1Γα+1supxa,bω1Dx-αf,1nβa,x+supxa,bω1Dxαf,1nβx,bnαβ+1eμn1-β-2b-aαsupxa,bDx-αf+supxa,bDxαf,x,b=:En. 128

Hence it holds

Lnf-f4(1+e-2μ)1-e-2μEn. 129

We observe the following:

We have

Dx-αfy=-1NΓN-αyxJ-yN-α-1fNJdJ,ya,x 130

and

Dx-αfy1ΓN-αyxJ-yN-α-1dJfN=1ΓN-αx-yN-αN-αfN=x-yN-αΓN-α+1fNb-aN-αΓN-α+1fN. 131

That is

Dx-αf,a,xb-aN-αΓN-α+1fN, 132

and

supxa,bDx-αf,a,xb-aN-αΓN-α+1fN. 133

Similarly we have

Dxαfy=1ΓN-αxyy-tN-α-1fNtdt,yx,b. 134

Thus we get

Dxαfy1ΓN-αxyy-tN-α-1dtfN 135
1ΓN-αy-xN-αN-αfNb-aN-αΓN-α+1fN.

Hence

Dxαf,x,bb-aN-αΓN-α+1fN, 136

and

supxa,bDxαf,x,bb-aN-αΓN-α+1fN. 137

From (89) and (90) we get

supxa,bω1Dx-αf,1nβa,x2fNΓN-α+1b-aN-α, 138

and

supxa,bω1Dxαf,1nβx,b2fNΓN-α+1b-aN-α. 139

That is En<.

We finally notice that

Lnf,x-j=1N-1fjxj!Ln·-xjx-fx=Lnf,xk=nanbGnx-k-1k=nanbGnx-k·j=1N-1fjxj!Ln·-xjx-fx
=1k=nanbGnx-kLnf,x-j=1N-1fjxj!Ln·-xjx 140
-k=nanbGnx-kfx.

Therefore we get

Lnf,x-j=1N-1fjxj!Ln·-xjx-fx4(1+e-2μ)1-e-2μ·
Lnf,x-j=1N-1fjxj!Ln·-xjx-k=nanbGnx-kfx, 141

xa,b.

The proof of the theorem is now finished.

Next we apply Theorem 30 for N=1.

Theorem 31

Let 0<α,β<1, μ>0, fC1a,b,X, xa,b, nN:n1-β>2. Then

  • (i)
    Lnf,x-fx4(1+e-2μ)(1-e-2μ)1Γα+1ω1Dx-αf,1nβa,x+ω1Dxαf,1nβx,bnαβ+1eμn1-β-2Dx-αf,a,xx-aα+Dxαf,x,bb-xα, 142
    and
  • (ii)
    Lnf-f4(1+e-2μ)(1-e-2μ)1Γα+1·supxa,bω1Dx-αf,1nβa,x+supxa,bω1Dxαf,1nβx,bnαβ+b-aαeμn1-β-2supxa,bDx-αf,a,x+supxa,bDxαf,x,b. 143

When α=12 we derive

Corollary 32

Let 0<β<1, μ>0, fC1a,b,X, xa,b, nN:n1-β>2. Then

  • (i)
    Lnf,x-fx8(1+e-2μ)(1-e-2μ)πω1Dx-12f,1nβa,x+ω1Dx12f,1nβx,bnβ2+eμn1-β-2Dx-12f,a,xx-a+Dx12f,x,bb-x, 144
    and
  • (ii)
    Lnf-f8(1+e-2μ)(1-e-2μ)π·supxa,bω1Dx-12f,1nβa,x+supxa,bω1Dx12f,1nβx,bnβ2+b-aeμn1-β-2supxa,bDx-12f,a,x+supxa,bDx12f,x,b<. 145

We make

Remark 33

Some convergence analysis follows based on Corollary 32.

Let 0<β<1, μ>0, fC1a,b,X, xa,b, nN:n1-β>2. We elaborate on ( 145). Assume that

ω1Dx-12f,1nβa,xR1nβ, 146

and

ω1Dx12f,1nβx,bR2nβ, 147

xa,b, nN, where R1,R2>0.

Then it holds

supxa,bω1Dx-12f,1nβa,x+supxa,bω1Dx12f,1nβx,bnβ2R1+R2nβnβ2=R1+R2n3β2=Rn3β2, 148

where R:=R1+R2>0.

The other summand of the right hand side of (145), for large enough n, converges to zero at the speed 1eμn1-β-2, so it is about Leμn1-β-2, where L>0 is a constant.

Then, for large enough nN, by (145), (148) and the above comment, we obtain that

Lnf-fMn3β2, 149

where M>0, converging to zero at the high speed of 1n3β2.

In Theorem 11, for fCa,b,X and for large enough nN, the speed is 1nβ. So by (149), Lnf-f converges much faster to zero. The last comes because we assumed differentiability of f. Notice that in Corollary 32 no initial condition is assumed.

Footnotes

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Contributor Information

George A. Anastassiou, Email: ganastss@memphis.edu

Seda Karateke, Email: sedakarateke@topkapi.edu.tr.

References

  • 1.Anastassiou GA. Rate of convergence of some neural network operators to the unit-univariate case. J. Math. Anal. Appl. 1997;212:237–262. doi: 10.1006/jmaa.1997.5494. [DOI] [Google Scholar]
  • 2.Anastassiou GA. Quantitative Approximations. Boca Raton, New York: Chapman & Hall/CRC; 2001. [Google Scholar]
  • 3.Anastassiou, G.A.: Inteligent Systems: Approximation by Artificial Neural Networks, Intelligent Systems Reference Library, vol. 19. Springer, Heidelberg (2011)
  • 4.Anastassiou GA. Univariate hyperbolic tangent neural network approximation. Math. Comput. Model. 2011;53:1111–1132. doi: 10.1016/j.mcm.2010.11.072. [DOI] [Google Scholar]
  • 5.Anastassiou GA. Multivariate hyperbolic tangent neural network approximation. Comput. Math. 2011;61:809–821. [Google Scholar]
  • 6.Anastassiou GA. Multivariate sigmoidal neural network approximation. Neural Netw. 2011;24:378–386. doi: 10.1016/j.neunet.2011.01.003. [DOI] [PubMed] [Google Scholar]
  • 7.Anastassiou GA. Univariate sigmoidal neural network approximation. J. Comput. Anal. Appl. 2012;14(4):659–690. [Google Scholar]
  • 8.Anastassiou GA. Fractional neural network approximation. Comput. Math. Appl. 2012;64:1655–1676. doi: 10.1016/j.camwa.2012.01.019. [DOI] [Google Scholar]
  • 9.Anastassiou GA. Intelligent Systems II: Complete Approximation by Neural Network Operators. Heidelberg, New York: Springer; 2016. [Google Scholar]
  • 10.Anastassiou GA. Strong right fractional calculus for banach space valued functions. Revista Proyecciones. 2017;36(1):149–186. doi: 10.4067/S0716-09172017000100009. [DOI] [Google Scholar]
  • 11.Anastassiou GA. Vector fractional Korovkin type Approximations. Dyn. Syst. Appl. 2017;26:81–104. [Google Scholar]
  • 12.Anastassiou GA. A strong fractional calculus theory for Banach space valued functions. Nonlinear Funct. Anal. Appl. (Korea) 2017;22(3):495–524. [Google Scholar]
  • 13.Anastassiou GA. Nonlinearity: Ordinary and Fractional Approximations by Sublinear and Max-Product Operators. Heidelberg, New York: Springer; 2018. [Google Scholar]
  • 14.Chen Z, Cao F. The approximation operators with sigmoidal functions. Comput. Math. Appl. 2009;58:758–765. doi: 10.1016/j.camwa.2009.05.001. [DOI] [Google Scholar]
  • 15.Haykin S. Neural Networks: A Comprehensive Foundation. 2. New York: Prentice Hall; 1998. [Google Scholar]
  • 16.Kreuter, M.: Sobolev Spaces of Vector-valued functions, Ulm Univ., Master Thesis in Math., Ulm, Germany (2015)
  • 17.Lee SY, Lei B, Mallick B. Estimation of COVID-19 spread curves integrating global data and borrowing information. PLoS One. 2020;15(7):e0236860. doi: 10.1371/journal.pone.0236860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.McCulloch W, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 1943;7:115–133. doi: 10.1007/BF02478259. [DOI] [PubMed] [Google Scholar]
  • 19.Mikusinski J. The Bochner Integral. New York: Academic Press; 1978. [Google Scholar]
  • 20.Mitchell TM. Machine Learning. New York: WCB-McGraw-Hill; 1997. [Google Scholar]
  • 21.Richards FJ. A Flexible Growth Function for Empirical Use. J. Exp. Bot. 1959;10(29):290–300. doi: 10.1093/jxb/10.2.290. [DOI] [Google Scholar]
  • 22.Shilov GE. Elementary Functional Analysis. New York: Dover Publications Inc; 1996. [Google Scholar]

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