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Research article

Pseudo almost periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales

  • Received: 24 February 2021 Accepted: 05 July 2021 Published: 07 July 2021
  • MSC : 34K14, 34K20, 34N05, 92B20

  • This paper deals with a class of quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales. Based on the Banach fixed point theorem and the theory of calculus on time scales, some sufficient conditions are obtained for the existence and global exponential stability of pseudo almost periodic solutions for the considered networks. The results of this paper are completely new. Finally, an example is presented to illustrate the effectiveness of the obtained results.

    Citation: Xiaofang Meng, Yongkun Li. Pseudo almost periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales[J]. AIMS Mathematics, 2021, 6(9): 10070-10091. doi: 10.3934/math.2021585

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  • This paper deals with a class of quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales. Based on the Banach fixed point theorem and the theory of calculus on time scales, some sufficient conditions are obtained for the existence and global exponential stability of pseudo almost periodic solutions for the considered networks. The results of this paper are completely new. Finally, an example is presented to illustrate the effectiveness of the obtained results.



    Because high-order Hopfield neural networks have more extensive applications than Hopfield neural networks, various dynamical behaviours of high-order Hopfield neural networks such as the existence and stability of equilibrium points [1,2], anti-periodic solutions [3], almost periodic solutions [4,5,6] and pseudo almost periodic solutions [7] have been studied by many scholars.

    On the one hand, due to the limited switching speed of neurons and amplifiers, time delays are inevitably introduced into neural network models [8,9,10,11,12]. Among all kinds of time delays, the leakage delay, that is, the time delay in the leakage term, has been proved to have a great influence on the dynamics of the system. Therefore, it is significant to consider neural networks with time delays in leakage terms [13,14,15,16].

    On the other hand, both continuous-time and discrete-time neural networks have equally importance in various applications. Therefore, it is necessary to consider both continuous time neural networks and discrete time neural networks. Fortunately, the theory of time scale calculus [17] can unify the study of continuous analysis and discrete analysis, so the study of neural network models on time scale can unify the study of continuous-time and discrete-time neural networks [18,19,20,21].

    In addition, quaternion-valued neural networks, as an extension of real-valued neural networks and complex-valued neural networks, have been extensively applied in many fields such as robotics, satellite attitude control, computer graphics, ensemble control and so on [22,23,24]. Currently, the study quaternion-valued neural networks have received much attention of many scholars.

    Moreover, although non-autonomous neural networks are more general and practical than autonomous neural networks, so far, there are still few results about the dynamic behavior of non-autonomous quaternion-valued neural networks [25,26,27,28,29]. It is well known that periodicity, almost periodicity and pseudo almost periodicity are very important dynamic behaviors of non-autonomous systems. Besides, almost periodicity is more reasonable than periodicity. Also, pseudo almost periodicity is more complex than almost periodicity [30,31]. Therefore, for non-autonomous neural networks, pseudo almost periodic oscillation is a very important dynamics [32,33,34,35,36,37].

    However, up to now, there has been no paper published on the pseudo almost periodic oscillation of quaternion-valued high-order Hopfield neural networks. Besides, the pseudo almost periodic oscillation of quaternion-valued neural networks with quaternion leakage coefficients on time scales has not been reported. Consequently, it is necessary to study the pseudo almost periodic solutions of quaternion-valued high-order Hopfield neural networks on time scales whose leakage coefficients are also quaternions.

    Motivated by the above statement, in this paper, we consider the following quaternion-valued high-order Hopfield neural network with time-varying delays and leakage delays on time scales:

    xΔp(t)=ap(t)xp(tηp(t))+nq=1bpq(t)fq(xq(t))+nq=1cpq(t)gq(xq(tτpq(t)))+nq=1nl=1Tpql(t)hq(xq(tδpql(t)))hl(xl(tϑpql(t)))+up(t),tt0,tT, (1.1)

    where p{1,2,,n}=:S, n is the number of neurons in layers; xp(t) denotes the activation of the pth neuron at time t; ap(t)Q represents the rate with the pth unit will reset its potential to the resting state in isolation when disconnected from the network and external inputs at time t; bpq(t),cpq(t)Q are the delay connection weights from neuron q to neuron p at time t, respectively; Tpql(t)Q denotes the second-order connection weights of the neural network; fq,gq,hq:QQ are the activation functions of signal transmission; up(t)Q is the external input on the pth unit at time t; ηp denotes the leakage delay at time t and satisfies tηp(t)T; τpq, δpql and ϑpql are transmission delays at time t and satisfy tτpq(t)T, tδpql(t)T and tϑpql(t)T for tT.

    The skew field of quaternions is denoted by

    Q:={q=qR+iqI+jqJ+kqK},

    where qR, qI, qJ, qK are real numbers, the three imaginary units i, j and k obey the Hamilton's multiplication rules:

    ij=ji=k,jk=kj=i,ki=ik=j,i2=j2=k2=ijk=1.

    Throughout this paper, for x=xR+ixI+jxJ+kxKQ, we denote ˆx=xRx, xQ=max{|xR|,|xI|,|xJ|,|xK|}, and for x=(x1,x2,,xn)TQn, we denote xQn=maxpS{xpQ}. Also, for convenience, we introduce the following notation:

    ap=inftT{aRp(t)},a+p=suptT{aRp(t)},ˆa+p=suptTˆap(t)Q,
    b+pq=suptTbpq(t)Q,c+pq=suptTcpq(t)Q,T+pql=suptTTpql(t)Q,
    η+=maxpS{suptTηp(t)},τ+=maxp,qS{suptTτpq(t)},δ+=maxp,q,lS{suptTδpql(t)},
    ϑ+=maxp,q,lS{suptTϑpql(t)},θ=max{η+,τ+,δ+,ϑ+}.

    The initial condition of system (1.1) is of the form

    xp(s)=ϕp(s),xΔp(s)=ψp(s),s[t0θ,t0]T,

    where ϕp,ψΔpC([t0θ,t0]T,Q), pS.

    Our main aim of this paper is to study the existence and stability of pseudo almost periodic solutions of (1.1). The main contributions of this paper are listed as follows. Firstly, this is the first time to consider quaternion-valued neural networks on time scales with all the coefficients are quaternions except time delays. Secondly, this is the first paper to study the pseudo almost periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales. Finally, our method of this paper can be used to study pseudo almost periodic solutions for other types of quaternion-valued neural networks on time scales.

    This paper is organized as follows: In Section 2, we introduce some definitions, preliminary lemmas. In Section 3, we establish some sufficient conditions for the existence and global exponential stability of pseodo almost periodic solutions of system (1.1). In Section 4, we give an example to demonstrate the feasibility of our results. This paper ends with a brief conclusion in Section 5.

    Definition 2.1. [38,39] A time scale T is an arbitrary nonempty closed subset of the real set R with the topology and ordering inherited from R. The forward and backward jump operators σ,ρ:TT, and the forward graininess μ:T[0,) are defined, respectively, by

    σ(t)=inf{sT,s>t},ρ(t)=sup{sT,s<t},μ(t)=σ(t)t.

    The point tT is called left-dense, left-scattered, right-dense or right-scattered if ρ(t)=t, ρ(t)<t,σ(t)=t or σ(t)>t, respectively. Points that are right-dense and left-dense at the same time are called dense. If T has a left-scattered maximum m, define Tκ=T{m}; otherwise, set Tκ=T. If T has a right-scattered maximum m, define Tκ=T{m}; otherwise, set Tκ=T.

    Definition 2.2. [38,39] Assume that f:TR is a function and let tTk. Then we define fΔ(t) to be the number (provided it exists) with the property that given any ε>0, there is 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. Moreover, we say that f is delta (or Hilger) differentiable (or in short: differentiable) on Tk provided fΔ(t) exists for all tTk. The function fΔ:TkR is then called the (delta) derivative of f on Tk.

    The derivative of function f(t)=fR(t)+ifI(t)+jfJ(t)+kfK(t):TQ is given by

    fΔ(t)=(fR)Δ(t)+i(fI)Δ(t)+j(fJ)Δ(t)+k(fK)Δ(t),

    where fR,fI,fJ,fK:TR.

    Definition 2.3. [38,39] A function p:TR is said to be regressive provided

    1+μ(t)p(t)0,tTκ.

    The set of all positive regressive and rd-continuous functions p:TR are denoted by

    R+={pR:1+μ(t)p(t)>0,tT}.

    Definition 2.4. [38,39] If pR+, then we define the exponential function by

    ep(t,s)=exp(tsξμ(τ)(p(τ))Δτ),t,sT,

    with the cylinder transformation

    ξh(z)={Log(1+hz)h,ifh0,z,ifh=0.

    Definition 2.5. [38,39] Let p,q:TR be two regressive functions, define

    pq=p+q+μpq,p=p1+μp,pq=p(q).

    Lemma 2.1. [38,39] Let pR, and t,s,rT. Then

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

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

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

    (iv) (ep(t,s))Δ=(p)(t)ep(t,s).

    Definition 2.6. [18] A time scale T is called an almost periodic time scale if

    Π:={τR:t±τT,tT}{0}.

    We denote by BC(T,Qn) the set of all bounded continuous functions from T to Qn. Similar to Definition in [18], we give the following definition.

    Definition 2.7. Let T be an almost periodic time scale. A function fBC(T,Qn) is called an almost periodic on T if the ε-translation set of

    T(ε,f)={τΠ:f(t+τ)f(t)0<ε,tT}

    is a relatively dense set in R for all ε>0; that is, for any given ε>0, there exists a constant l(ε)>0 such that each interval of length l(ε) contains at least one τ(ε)T(ε,f) such that

    f(t+τ)f(t)0<ε,tT.

    We denote by AP(T,Qn) the set of all almost periodic functions defined on T.

    Define the class of functions PAP0(T,Qn) as follows:

    PAP0(T,Qn)={fBC(T,Qn):fisΔmeasurablesuchthatlimr+12rrrf(t)0Δt=0,whererT}.

    Similar to Definition in [35], we give the following definition.

    Definition 2.8. A function fBC(T,Qn) is called pseudo almost periodic if f=g+h, where gAP(T,Qn) and hPAP0(T,Qn).

    We denote by PAP(T,Qn) the set of all pseudo almost periodic functions from T to Qn.

    Similar to the proofs in [21], it is not difficult to prove the following lemmas.

    Lemma 2.2. If f,gPAP(T,Qn), then f+g,fgPAP(T,Qn); if fPAP(T,Qn), gAP(T,Qn), then fgPAP(T,Qn).

    Lemma 2.3. If fC(Q,Q) satisfies the Lipschitcz condition, φPAP(T,Q) and τC1(T,Π)AP(T,R+) with inftT{1τΔ(t)}>0, then f(φ(τ()))PAP(T,Q).

    Throughout this paper, we assume that the following conditions hold:

    (H1) aRpAP(T,R+) with aRpR+, apAP(T,Q), bpq,cpq,Tpql,upPAP(T,Q), ηp,τpq,δpql,ϑpqlC1(T,Π)AP(T,R+) with inftT{1ηΔp(t)}>0, inftT{1τΔpq(t)}>0, inftT{1δΔpql(t)}>0, inftT{1ϑΔpql(t)}>0, where p,q,lS.

    (H2) There exist positive constants Lfq,Lgq,Lhq,Mhq such that for any x,yQ,

    fq(x)fq(y)QLfqxyQ,gq(x)gq(y)QLgqxyQ,
    hq(x)hq(y)QLhqxyQ,hq(x)QMhq

    and fq(0)=gq(0)=hq(0)=0, where qS.

    (H3) maxpS{Ξpap,(1+a+pap)Ξp}=:ρ<1, where

    Ξp=a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pql(MhqLhl+LhqMhl).

    Let E={ϕ=(ϕ1,ϕ2,,ϕn)T|ϕ,ϕΔPAP(T,Qn)} with the norm

    ϕE=maxpS{ϕ0,ϕΔ0},

    where ϕ0=suptTmaxpS{ϕp} and ϕp=suptTϕp(t)Q, then E is a Banach space.

    Set ϕ0=(ϕ01,ϕ02,,ϕ0n)T, where

    ϕ0p(t)=teaRp(t,σ(s))up(s)ds,tT,pS

    and κ is a constant satisfying κϕ0E.

    Lemma 3.1. Let (H1) hold, then every bounded solution x=(x1,x2,,xn)T of system (1.1) is a solution of the following system:

    xp(t)=teaRp(t,σ(s))[aRp(s)ssηp(s)xΔp(u)Δu+ˆap(s)xp(sηp(s))+nq=1bpq(s)fq(xq(s))+nq=1cpq(s)gq(xq(sτpq(s)))+nq=1nl=1Tpql(s)hq(xq(sδpql(s)))hl(xl(sϑpql(s)))+up(s)]Δs, (3.1)

    where pS, tT, and vice versa.

    Proof. On the one hand, if x=(x1,x2,,xn)T is a solution of (3.1), then by Δ-differentiate both sides of (3.1), we see that x=(x1,x2,,xn)T is also a solution of (1.1).

    On the other hand, let x be a bounded solution of (1.1), then for pS,

    xΔp(t)=aRp(t)xp(t)+aRp(t)ttηp(t)xΔp(s)Δs+ˆap(t)xp(tηp(t))+nq=1bpq(t)fq(xq(t))+nq=1cpq(t)gq(xq(tτpq(t)))+nq=1nl=1Tpql(t)hq(xq(tδpql(t)))hl(xl(tϑpql(t)))+up(t),tT. (3.2)

    Multiply both sides of (3.2) by eaRp(t0,σ(t)), we can get

    [xp(t)eaRp(t0,t)]Δ=eaRp(t0,σ(t))[aRp(t)ttηp(t)xΔp(s)Δs+ˆap(t)xp(tηp(t))+nq=1bpq(t)fq(xq(t))+nq=1cpq(t)gq(xq(tτpq(t)))+nq=1nl=1Tpql(t)hq(xq(tδpql(t)))hl(xl(tϑpql(t)))+up(t)], (3.3)

    where tt0, t0T. Integrating both sides of (3.3) from t0 to t, we obtain

    xp(t)=eaRp(t,t0)xp(t0)+tt0eaRp(t,σ(s))[aRp(s)ssηp(s)xΔp(u)Δu+ˆap(s)xp(sηp(s))+nq=1bpq(s)fq(xq(s))+nq=1cpq(s)gq(xq(sτpq(s)))+nq=1nl=1Tpql(s)hq(xq(sδpql(s)))hl(xl(sϑpql(s)))+up(s)]Δs.

    Letting t0, then we obtain that (1.1) holds. The proof is complete.

    Theorem 3.1. Let (H1)-(H3) hold. Then system (1.1) has a unique pseudo almost periodic solution in E={ϕϕE|ϕϕ0Eκρ1ρ}.

    Proof. For any ϕE, we define a mapping Φ:EPAP(T,Qn) by setting

    (ϕ1,ϕ2,,ϕn)(xϕ1,xϕ2,,xϕn),

    where

    xϕp(t)=teaRp(t,σ(s))[aRp(s)ssηp(s)ϕΔp(u)Δu+ˆap(s)ϕp(sηp(s))+nq=1bpq(s)fq(ϕq(s))+nq=1cpq(s)gq(ϕq(sτpq(s)))+nq=1nl=1Tpql(s)hq(ϕq(sδpql(s)))hl(ϕl(sϑpql(s)))+up(s)]Δs,pS.

    First, we will prove that Φ maps E into itself. To this end, let

    Fp(s)=aRp(s)ssηp(s)ϕΔp(u)Δu+ˆap(s)ϕp(sηp(s))+nq=1bpq(s)fq(ϕq(s))+nq=1cpq(s)gq(ϕq(sτpq(s)))+nq=1nl=1Tpql(s)hq(ϕq(sδpql(s)))×hl(ϕl(sϑpql(s)))+up(s),pS.

    Then, by Lemmas 2.2 and 2.3, we find that Fp(s)PAP(T,Q). So, for all pS, we can set Fp(s)=F1p(s)+F0p(s), where F1pAP(T,Q) and F0pPAP0(T,Q). We shall show that xϕpPAP(T,Q), that is, xϕp can be expressed as

    xϕp(t)=teaRp(t,σ(s))F1p(s)Δs+teaRp(t,σ(s))F0p(s)Δs:=Ω1p(t)+Ω0p(t),pS,

    where Ω1pAP(T,Q) and Ω0pPAP0(T,Q).

    In fact, since aRpAP(T,R+) and F1pAP(T,Q), for every ε>0, there exists l>0 such that every interval of length l contains a number τΠ satisfying

    |aRp(t+τ)aRp(t)|<ε,F1p(t+τ)F1p(t)Q<ε,pS,tT.

    Consequently, we have

    F1p(t+τ)F1p(t)Q=teaRp(t+τ,σ(s+τ))F1p(s+τ)ΔsteaRp(t,σ(s))F1p(s)ΔsQteaRp(t+τ,σ(s+τ))F1p(s+τ)ΔsteaRp(t+τ,σ(s+τ))F1p(s)ΔsQ+teaRp(t+τ,σ(s+τ))F1p(s)ΔsteaRp(t,σ(s))F1p(s)ΔsQt|eaRp(t+τ,σ(s+τ))|F1p(s+τ)F1p(s)QΔs+t|eaRp(t+τ,σ(s+τ))eaRp(t,σ(s))|F1p(s)QΔs<εap+F1p(ap)2ε,pS,

    which implies that Ω1pAP(T,Q). Then, we will prove that Ω0pPAP0(T,Q). In addition, from F0pPAP0(T,Q), rT, we have

    limr+12rrrΩ0p(t)QΔt=limr+12rrrteaRp(t,σ(s))F0p(s)ΔsQΔtlimr+12rteaRp(t,σ(s))(rrF0p(s)QΔs)Δt,pS,

    which implies that Ω0pPAP0(T,Q). Therefore, xϕpPAP(T,Q), that is, Φ maps E into PAP(T,Qn).

    Next, we will show that Φ is a self-mapping from E to E. In fact, for each ϕE, we have

    (Φϕ)(t)ϕ0(t)Qn=maxpS{teaRp(t,σ(s))[aRp(s)ssηp(s)ϕΔp(u)Δu+ˆap(s)ϕp(sηp(s))+nq=1bpq(s)fq(ϕq(s))+nq=1cpq(s)gq(ϕq(sτpq(s)))+nq=1nl=1Tpql(s)hq(ϕq(sδpql(s)))hl(ϕl(sϑpql(s)))]ΔsQ}maxpS{teap(t,σ(s))[a+pη+pϕΔp+ˆap(s)Qϕp(sηp(s))Q+nq=1bpq(s)QLfqϕq(s)Q+nq=1cpq(s)QLgjϕq(sτpq(s))Q+nq=1nl=1Tpql(s)QMhlLhqϕq(sδpql(s)))Q]Δs}maxpS{1ap[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pqlMhlLhq]}ϕE.

    Thus, we have

    Φϕϕ00=suptT(Φϕ)(t)ϕ0(t)QnmaxpS{1ap[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pqlMhlLhq]}ϕE. (3.4)

    On the other hand, we have

    [Φϕϕ0]Δ0=suptT[(Φϕ)(t)ϕ0(t)]ΔQn=maxpS{suptTFp(t)aRp(t)teaRp(t,σ(s))Fp(s)ΔsQ}maxpqS{[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pqlMhlLhq]+a+pap[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pqlMhlLhq]}ϕE. (3.5)

    Noting the fact that for ϕE, we have

    ϕEϕ0E+ϕϕ0Eκ+κρ1ρκ1ρ.

    It follows from (3.4)-(3.5), and (H3) that

    Φϕϕ0Eκρ1ρ,

    thus, we have ΦϕE.

    Finally, we will show that Φ is a contraction mapping in E. For any ϕ,ψE, we can get

    (Φϕ)(t)(Φψ)(t)Qn=maxpS{teaRp(t,σ(s))(aRp(s)ssηp(s)[ϕΔp(u)ψΔp(u)]Δu+ˆap(s)[ϕp(sηp(s))ψp(sηp(s))]+nq=1bpq(s)[fq(ϕq(s))fq(ψq(s))]+nq=1cpq(s)[gq(ϕq(sτpq(s)))gq(ψq(sτpq(s)))]+nq=1nl=1Tpql(s)[hq(ϕq(sδpql(s)))hl(ϕl(sϑpql(s)))hq(ϕq(sδpql(s)))hl(ϕl(sϑpql(s)))])ΔsQ}maxpS{teap(t,σ(s))(a+pη+pϕΔpψΔp+ˆap(s)Qϕp(sηp(s))ψp(sηp(s))Q+nq=1bpq(s)QLfqϕq(s)ψq(s)Q+nq=1cpq(s)QLgq×ϕq(sτpq(s))ψq(sτpq(s))Q+nq=1nl=1Tpql(s)Q×[hq(ϕq(sδpql(s)))Qhl(ϕl(sϑpql(s)))hl(ψl(sϑpql(s)))Q+hq(ϕq(sδpql(s)))hq(ψq(sδpql(s)))Qhl(ψl(sϑpql(s)))Q])Δs}maxpS{1ap[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1Tpql(MhqLhl+LhqMhl)]}ϕψE. (3.6)

    It follows from (3.6) that

    ΦϕΦψ0maxpS{1ap[a+pη+p+ˆa+p+nq=1b+pqLfq+nq=1c+pqLgq+nq=1nl=1T+pql(MhqLhl+LhqMhl)]}ϕψE=maxpS{Ξpap}ϕψE. (3.7)

    On the other hand, we can derive that

    [ΦϕΦψ]Δ0maxpS{Ξp+a+papΞp}ϕψE. (3.8)

    By (3.7), (3.8) and (H3), we have

    ΦϕΦψEρϕψE,

    in view of the definition of ρ, which implies that Φ is a contraction mapping. Therefore, Φ has a unique fixed point in E, that is, (1.1) has a unique pseudo almost periodic solution in E. The proof is complete.

    Definition 3.1. Let x=(x1,x2,,xn)T be a solution of (1.1) with the initial value ϕ=(ϕ1,ϕ2,,ϕn)T. If there exist positive constants λ>0 and M>0 such that every solution y=(y1,y2,,yn)T of (1.1) with initial value ψ=(ψ1,ψ2,,ψn)T satisfies

    y(t)x(t)1Meλ(t,t0)ψϕθ,t(t0,+)T,

    where

    y(t)x(t)1=max{y(t)x(t)Qn,[y(t)x(t)]ΔQn},
    ψϕθ=max{sups[t0θ,t0]Tψ(s)ϕ(s)Qn,sups[t0θ,t0]T[ψ(s)ϕ(s)]ΔQn},

    then the solution x is said to be globally exponentially stable.

    Theorem 3.2. Assume that (H1)-(H3) hold, then system (1.1) has a unique pseudo almost periodic solution that is globally exponentially stable.

    Proof. From Theorem 3.1, we see that system (1.1) has a pseudo almost periodic solution x(t)=(x1(t),x2(t),,xn(t))T with initial value ϕ(s)=(ϕ1(s),ϕ2(s),,ϕn(s))T. Suppose that y(t)=(y1(t),y2(t),,yn(t))T is an arbitrary solution of system (1.1) with initial value ψ(s)=(ψ1(s),ψ2(s),,ψn(s))T and let z(t)=y(t)x(t), then we have

    zΔp(t)=aRp(t)[yp(t)xp(t)]+aRp(t)ttηp(t)[yΔp(s)xΔp(s)]Δs+ˆap(t)[yp(tηp(t))xp(tηp(t))]+nq=1bpq(t)[fq(yq(t))fq(xq(t))]+nq=1cpq(t)[gq(yq(tτpq(t)))gq(xq(tτpq(t)))]+nq=1nl=1Tpql(t)[hq(yq(tδpql(t)))hl(yl(tϑpql(t)))hq(xq(tδpql(t)))hl(xl(tϑpql(t)))],pS,tT. (3.9)

    For pS, let Θp and Ψp be defined as follows:

    Θp(ω)=apωexp(ωsupsTμ(s))[a+pη+pexp(ωη+p)+ˆa+pexp(ωη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(ωτ+pq)+nq=1nl=1T+pql(MhqLhlexp(ωδ+pql)+LhqMhlexp(ωϑ+pql))]

    and

    Ψp(ω)=apω(a+pexp(ωsupsTμ(s))+ap)[a+pη+pexp(ωη+p)+ˆa+pexp(ωη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(ωτ+pq)+nq=1nl=1T+pql(MhqLhlexp(ωδ+pql)+LhqMhlexp(ωϑ+pql))].

    By (H3), we have

    Θp(0)=apΞp>0

    and

    Ψp(0)=ap(a+p+ap)Ξp>0.

    Based on the continuities of functions Θp and Ψp on [0,+), and the fact that Θp(ω),Ψp(ω), as ω+, there exist ζp,ξp>0 such that Θp(ζp)=Ψp(ξp)=0 and Θp(ω)>0 for ω(0,ζp), Ψp(ω)>0 for ω(0,ξp). Take γ=minpS{ζp,ξp}, we have Θp(γ)0, Ψp(γ)0. So, we can choose a positive constant 0<λ<min{γ,minpS{ap}} with λR+ such that

    Θp(λ)>0,Ψp(λ)>0,pS,

    which implies that

    exp(λsupsTμ(s))apλ[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]<1

    and

    (1+a+pexp(λsupsTμ(s))apλ)[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]<1,pS.

    Let M=maxpS{apΞp}, then by (H3), we have M>1. Thus,

    1MminpS{exp(λsupsTμ(s))apλ[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]}<0.

    Since eλ(t,t0)>1 for t[t0θ,t0]T, it is obvious that, for any ε>0,

    z(t0)1<(ψϕθ+ε)

    and

    z(t)1<(ψϕθ+ε)eλ(t,t0)<M(ψϕθ+ε)eλ(t,t0),t[t0θ,t0]T.

    We claim that

    z(t)1<M(ψϕθ+ε)eλ(t,t0),t(t0,+)T. (3.10)

    If (3.10) is not true, then there must be some t1(t0,+)T such that

    {z(t1)1M(ψϕθ+ε)eλ(t1,t0),z(t)1M(ψϕθ+ε)eλ(t,t0),t(t0,t1)T.

    Hence, there must exist a constant P1 such that

    {z(t1)1=PM(ψϕθ+ε)eλ(t1,t0),z(t)1PM(ψϕθ+ε)eλ(t,t0),t(t0,t1)T. (3.11)

    Multiplying both sides of (3.9) by eaRp(t0,σ(t)) and integrating over [t0,t]T, we get

    zp(t)=eaRp(t,t0)zp(t0)+tt0eaRp(t,σ(s))(aRp(s)ssηp(s)[yΔp(u)xΔp(u)]Δu+ˆap(s)[yp(sηp(s))xp(sηp(s))]+nq=1bpq(s)[fq(yq(s))fq(xq(s))]+nq=1cpq(s)[gq(yq(sτpq(s)))gq(xq(sτpq(s)))]+nq=1nl=1Tpql(s)[hq(yq(sδpql(s)))hl(yl(sϑpql(s)))hq(xq(sδpql(s)))hl(xl(sϑpql(s)))])Δs. (3.12)

    In view of (3.12) and M>1, we have

    z(t1)Qn=maxpS{zp(t1)Q}=maxpS{eaRp(t1,t0)zp(t0)+t1t0eaRp(t1,σ(s))(aRp(s)ssηp(s)[yΔp(u)xΔp(u)]Δu+ˆap(s)[yp(sηp(s))xp(sηp(s))]+nq=1bpq(s)[fq(yq(s))fq(xq(s))]+nq=1cpq(s)[gq(yq(sτpq(s)))gq(xq(sτpq(s)))]
    +nq=1nl=1Tpql(s)[hq(yq(sδpql(s)))hl(yl(sϑpql(s)))hq(xq(sδpql(s)))hl(xl(sϑpql(s)))])ΔsQ}maxpS{eaRp(t1,t0)zp(t0)Q+PMeλ(t1,t0)(ψϕ1+ε)×t1t0eaRpλ(t1,σ(s))[a+pssηp(s)eλ(σ(u),u)Δu+ˆa+peλ(σ(s),sηp(s))+nq=1b+pqLfqeλ(σ(s),s)+nq=1c+pqLgqeλ(σ(s),sτpq(s))+nq=1nl=1T+pql(MhqLhleλ(σ(s),sδpql(s))+LhqMhleλ(σ(s),sϑpql(s)))]Δs}maxpS{eaRp(t1,t0)zp(t0)Q+PMeλ(t1,t0)(ψϕ1+ε)×t1t0edpλ(t1,σ(s))[a+pη+pexp[λ(η+p+supsTμ(s))]
    +ˆa+pexp[λ(η+p+supsTμ(s))]+nq=1b+pqLfqexp(λsupsTμ(s))+nq=1c+pqLgqexp[λ(τ+pq+supsTμ(s))]+nq=1nl=1T+pql(MhqLhl×exp[λ(δ+pql+supsTμ(s))]+LhqMhlexp[λ(ϑ+pql+supsTμ(s))])]Δs}maxpS{PMeλ(t1,t0)(ψϕ1+ε){eaRpλ(t1,t0)PM+t1t0eaRpλ(t1,σ(s))×exp(λsupsTμ(s))[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)
    +nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]Δs}}<maxpS{PMeλ(t1,t0)(ψϕ1+ε){eaRpλ(t1,t0)M+1eaRpλ(t1,t0)apλ×exp(λsupsTμ(s))[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq
    +nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]}}=maxpS{PMeλ(t1,t0)(ψϕ1+ε){(1Mexp(λsupsTμ(s))apλ×[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))])×eaRpλ(t1,t0)+exp(λsupsTμ(s))apλ[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]}}<PMeλ(t1,t0)(ψϕθ+ε)

    and

    zΔ(t1)Qn=maxpS{zΔp(t1)Q}maxpS{a+peaRp(t1,t0)(ψϕ1+ε)+PMeλ(t1,t0)(ψϕ1+ε)×[a+pt1t1ηp(t1)eλ(σ(s),s)Δs+ˆa+peλ(σ(t1),t1ηp(t1))+nq=1b+pqLfqeλ(σ(t1),t1)+nq=1c+pqLgqeλ(σ(t1),t1τpq(t1))+nq=1nl=1T+pql(MhqLhleλ(σ(t1),t1δpql(t1))+LhqMhleλ(σ(t1),t1ϑpql(t1)))]+a+pPMeλ(t1,t0)(ψϕ1+ε)t1t0eaRpλ(t1,σ(s))×[a+pssηp(s)eλ(σ(u),u)Δu+ˆa+peλ(σ(s),sηp(s))+nq=1b+pqLfqeλ(σ(s),s)+nq=1c+pqLgqeλ(σ(s),sτpq(s))+nq=1nl=1T+pql(MhqLhleλ(σ(s),sδpql(s))+LhqMhleλ(σ(s),sϑpql(s)))]Δs}maxpS{a+peaRp(t1,t0)(ψϕ1+ε)+PMeλ(t1,t0)(ψϕ1+ε)×[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]×[1+a+pexp(λsupsTμ(s))t1t0eaRpλ(t1,σ(s))Δs]}maxpS{PMeλ(t1,t0)(ψϕ1+ε){[1Mexp(λsupsTμ(s))apλ×[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]eaRpλ(t1,t0)+(1+a+pexp(λsupsTμ(s))apλ)[a+pη+pexp(λη+p)+ˆa+pexp(λη+p)+nq=1b+pqLfq+nq=1c+pqLgqexp(λτ+pq)+nq=1nl=1T+pql(MhqLhlexp(λδ+pql)+LhqMhlexp(λϑ+pql))]}}<PMeλ(t1,t0)(ψϕθ+ε).

    The above two inequalities imply that

    z(t1)1<PMeλ(t1,t0)(ψϕθ+ε),

    which contradicts the first equation of (3.11). Therefore, (3.10) holds. Letting ε0+ leads to

    z(t)1Meλ(t,t0)ψϕθ,t(t0,+)T.

    Hence, the pseudo almost periodic solution of system (1.1) is globally exponentially stable. The proof is complete.

    Example 4.1. In system (1.1), let n=2,t0=0 and take

    fq(xq)=140sin(xRq+xIq+xKq)+i150sinxIq+j140sinxJq+k145sinxKq,
    gq(xq)=150sinxRq+i155sin(xIq+xKq)+j160sinxJq+k155sin(xRq+xKq),
    hq(xq)=130sinxRq+i120sinxIq+j125sinxJq+k125sin(xIq+xKq),
    a1(t)=0.4+0.1|cos2t|+0.2icos2t+(0.20.05cos3t)j+0.1ksin2t,
    a2(t)=0.60.1sin3t+0.3isin2t+0.2jsin3t+0.25kcos2t,
    b11(t)=b12(t)=0.2cos3t+0.2icos2t+0.4jsin2t+0.3ksin2t,
    b21(t)=b22(t)=0.4sin2t+0.2icos3t+0.5jsin3t+0.5ksin2t,
    c11(t)=c12(t)=0.5sin2t+0.5isint+0.8jcost+0.6kcos2t,
    c21(t)=c22(t)=0.8cost+11+t2+isin2t+0.5jsint+0.9kcos3t,
    T111(t)=T112(t)=0.4sin2t+0.3isint+0.5jsin3t+0.2kcos2t,
    T121(t)=T122(t)=1.2sint+1.5icost+0.7jcos2t+kcost,
    T211(t)=T212(t)=0.5cos2t+0.8isint+0.6jsin2t+0.7ksin2t,
    T221(t)=T222(t)=2cos2t+1.6icost+0.9jcos2t+ksin2t,
    u1(t)=0.2(sin3t+et2cos2t)+0.4icos2t+0.3jcos2t+0.3ksin3t,
    u2(t)=0.3(cos2t+et2cos2t)+0.4isin2t+0.35jsin2t+0.2kcos3t.

    If T=R, we take

    ηp(t)=0.1|sin2t|,τpq(t)=0.2|cost|,δpql(t)=ϑpql(t)=0.3|sin2t|.

    If T=Z, we take

    ηp(t)=2|cos(2πt)|,τpq(t)=3|sinπt|,δpql(t)=ϑpql(t)=2|sin(2πt)|.

    By a simple calculation, we have

    Lfq=140,Lgq=150,Lhq=Lhl=120,Mhq=Mhl=120,
    a1=0.4,a2=0.5,a+1=0.5,a+2=0.7,ˆa+1=0.2,ˆa+2=0.3,
    b+11=b+12=0.4,b+21=b+22=0.5,c+11=c+12=0.8,c+21=c+22=1,
    T+111=T+112=0.5,T+121=T+122=1.5,T+211=T+212=0.8,T+221=T+222=2.

    When T=R, we have

    η+p=0.1,τ+pq=0.2,δ+pql=ϑ+pql=0.3,Ξ1=0.322,Ξ2=0.463

    and

    max{Ξ1a1,(1+a+1a1)Ξ1,Ξ2a2,(1+a+2a2)Ξ2}=max{0.805,0.7245,0.926,0.6482}=0.926=ρ<1.

    When T=Z, we have

    η+p=0,τ+pq=3,δ+pql=ϑ+pql=2,Ξ1=0.272,Ξ2=0.393

    and

    max{Ξ1a1,(1+a+1a1)Ξ1,Ξ2a2,(1+a+2a2)Ξ2}=max{0.68,0.34,0.786,0.5502}=0.786=ρ<1.

    Hence, whether T=R or T=Z, all the conditions of Theorems 3.1 and 3.2 are satisfied. Consequently, we know that system (1.1) has a pseudo almost periodic solution, which is globally exponentially stable. Simulated by Matlab, when T=R and T=Z, Figures 1 and 2 show the time responses of the variables of system (1.1). Figure 1 has initial values

    (xR1(0),xR2(0))T=(0.5,0.1)T,(0.5,0.3)T,(0.1,0.2)T,
    (xI1(0),xI2(0))T=(0.2,0.5)T,(0.3,0.1)T,(0.4,0.2)T,
    (xJ1(0),xJ2(0))T=(0.1,0.1)T,(0.3,0.3)T,(0.5,0.5)T,
    (xK1(0),xK2(0))T=(0.25,0.3)T,(0.5,0.5)T,(0.15,0.1)T.
    Figure 1.  T=R. Transient states of the solutions xRp(t), xIp(t), xJp(t) and xKp(t), where p=1,2.
    Figure 2.  T=Z. Transient states of the solutions xRp(n), xIp(n), xJp(n) and xKp(n), where p=1,2.

    Figure 2 has initial values

    (xR1(0),xR2(0))T=(0.1,0.2)T,(0.2,0.3)T,(0.4,0.5)T,
    (xI1(0),xI2(0))T=(0.5,0.4)T,(0.3,0.1)T,(0.2,0.35)T,
    (xJ1(0),xJ2(0))T=(0.5,0.1)T,(0.1,0.3)T,(0.3,0.5)T,
    (xK1(0),xK2(0))T=(0.05,0.2)T,(0.4,0.5)T,(0.25,0.5)T.

    In this paper, we have established the existence and global exponential stability of pseudo almost periodic solutions for quaternion-valued high-order Hopfield neural networks with time-varying delays and leakage delays on time scales. The results of this paper are essentially new. In addition, we expect to extend this work to study other types of quaternion-valued neural networks on time scales.

    This work is supported by the National Natural Science Foundation of China under Grant No. 11861072.

    Both authors declare no conflicts of interest in this paper.



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