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Research article

The Weighted Lp estimates for the fractional Hardy operator and a class of integral operators on the Heisenberg group

  • Received: 03 October 2024 Revised: 06 January 2025 Accepted: 10 January 2025 Published: 15 January 2025
  • MSC : Primary 42B25; Secondary 42B20, 47H60, 47B47

  • In the setting of a Heisenberg group, we first studied the sharp weak estimate for the n-dimensional fractional Hardy operator from Lp to Lq,. Next, we studied the sharp bounds for the m-linear n-dimensional integral operator with a kernel on weighted Lebesgue spaces. As an application, the sharp bounds for Hardy, Hardy-Littlewood-Pólya, and Hilbert operators on weighted Lebesgue spaces were obtained. Finally, according to the previous steps, we also found the estimate for the Hausdorff operator on weighted Lp spaces.

    Citation: Tianyang He, Zhiwen Liu, Ting Yu. The Weighted Lp estimates for the fractional Hardy operator and a class of integral operators on the Heisenberg group[J]. AIMS Mathematics, 2025, 10(1): 858-883. doi: 10.3934/math.2025041

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  • In the setting of a Heisenberg group, we first studied the sharp weak estimate for the n-dimensional fractional Hardy operator from Lp to Lq,. Next, we studied the sharp bounds for the m-linear n-dimensional integral operator with a kernel on weighted Lebesgue spaces. As an application, the sharp bounds for Hardy, Hardy-Littlewood-Pólya, and Hilbert operators on weighted Lebesgue spaces were obtained. Finally, according to the previous steps, we also found the estimate for the Hausdorff operator on weighted Lp spaces.



    In a diverse world, the dynamics of neural networks has been studied extensively due to their convergence and stability are important in real applications, such as pattern recognition, image and signal processing, associative memories and so on [1,2,3]. A lot of literatures have been reported for different types of neural networks based on their various topological structures. To name a few, Cohen-Grossberg neural networks [4], bi-directonal associative memory (BAM) neural networks [5], Hopfield-type neural networks [6] and cellular neural networks [7].

    Among them, Cohen-Grossberg neural networks were originally introduced by Cohen and Grossberg in 1983, which can be reduced to Hopfield-type neural networks and cellular neural networks as well in [4]. And the dynamics of such neural networks have received increasing interest, such as stability, synchronization and stabilization [8,9,10,11,12]. In order to make a detailed description between different layers of networks, BAM neural networks have been proposed by Kosko in 1988, and many methods and techniques were developed to discuss the dynamical characteristics of BAM neural networks in [13,14,15,16]. Recently, a class of Cohen-Grossberg-type BAM neural networks have drawn significant attention owing to the wide application in practice [17,18,19,20].

    In fact, time delays occur in neural networks due to the finite speed of signal propagation. Generally, time delays can be divided into several types, such as discrete-type delays [21], distributed-type delays [22] and neutral-type delays [23]. As we all know, it is difficult to verify the dynamics of neutral delayed neural networks since they contain important information about the derivative of the past state. Some authors focused on neutral neural networks and several results have been obtained. For example, Cheng et al. [24] discussed neutral Cohen-Grossberg neural networks by means of Lyapunov stability method. Liu and Zong [25] dealt with a kind of neutral BAM neural networks based on some new integral inequalities and the Lyapunov-Krasovskii functional approach.

    On the other hand, many real-world systems often receive sudden external disturbance, which entail systems undergo abrupt changes in very short time. This phenomenon is viewed as impulse [26,27,28]. The existence of impulse is also one of the key factors leading to the instability of neural networks. Recently impulsive neural networks have aroused a lot of interest [29,30,31]. Gu et al. [32] established the existence and global exponential stability of BAM-type impulsive neural networks with time-varying delays. They mainly used the method of the continuation theorem of coincidence degree theory and Lyapunov functional analysis. Liao et al. [33] gave the results of global asymptotic stability of periodic solutions for inertial delayed BAM neural networks by combining Mawhin continuation theorem of coincidence degree theory, Lyapunov functional method and inequality techniques. Especially, they seek periodic solutions by means of Lyapunov functional method instead of the prior estimate method. The addition of delays and impulses in neural networks make it more accurate to describe the evolutionary process of the systems.

    To the best of our knowledge, Cohen-Grossberg-type BAM neural networks with neutral delays and impulses have not been investigated. The aim of this paper is to establish the existence and asymptotic stability of periodic solutions.

    To study the dynamics of neural networks, many methods and techniques are developed, such as the matrix theory, set-valued maps theory and functional differential inclusions. Our results are based on the famous Mawhin coincidence degree theory and Lyapunov functional analysis. To do this, our highlights lie in four aspects:

    Proposing a new model with neutral-type time delays and impulses, some previous considered neural network models can be regarded as the special cases of ours, such as [15,20,32].

    Establishing some sufficient conditions to guarantee the existence and asymptotic stability of the periodic solutions by means of Mawhin coincidence degree theory and the contraction of a suitable Lyapunov functional. We not only employ the method of the prior classical estimation in Section 3, but also seek periodic solutions by means of Lyapunov functional method in Appendix.

    The impulse terms in this paper are more relaxing from linear functions as well in [16].

    The theoretical findings play a key role in designing the electric implementation of Cohen-Grossberg-type BAM neural networks and processing its signals transmission.

    This paper is organized as follows. In Section 2, the model description and necessary knowledge are provided. In Section 3, by using the continuation theorem of coincidence degree theory, some conditions for the existence of periodic solutions are obtained. In Section 4, the global asymptotic stability of periodic solutions is discussed. In Section 5, an illustrative example is given to show the effectiveness of our criterions.

    Consider the following Cohen-Grossberg-type BAM neural networks with neutral-type time delays and impulses, i.e.,

    {˙xi(t)=ai(xi(t))[bi(xi(t))mj=1aij(t)fj(yj(tτij(t)))mj=1bij(t)fj(˙yj(t˜τij(t)))Ii(t)],t>0,ttk,Δxi(tk)=xi(t+k)xi(tk)=Iik(xi(tk)),i=1,2,,n,k=1,2,,˙yj(t)=cj(yj(t))[dj(yj(t))ni=1cji(t)gi(xi(tσji(t)))ni=1dji(t)gi(˙xi(t˜σji(t)))Jj(t)],t>0,ttk,Δyj(tk)=yj(t+k)yj(tk)=Jjk(yj(tk)),j=1,2,,m,k=1,2,. (2.1)

    The initial conditions associated with (2.1) are of the form

    {xi(t)=φi(t),t(τ,0],τ=max1in,1jm{τij},i=1,2,,n,yj(t)=ψj(t),t(σ,0],σ=max1in,1jm{σji},j=1,2,,m, (2.2)

    where

    τij=max0tω{τij(t),˜τij(t)}, and σji=max0tω{σji(t),˜σji(t)}.

    Obviously Δxi(tk) and Δyj(tk) are the impulses at moments tk and t1<t2< is a strictly increasing sequence such that limk+tk=+.

    The ecological meaning of parameters are as follows. Among the system (2.1), xi(t), yj(t) represent the potential (or voltage) of cell i, j at time t respectively; n, m correspond to the number of neurons in the Xlayer and Ylayer; ai(), cj() denote amplification functions; bi(), dj() mean appropriately behaved functions such that the solutions of system (2.1) remain bounded; aij(t), bij(t), cji(t), dji(t) describe the connection strengths of connectivity between cell i and j at the time t; fj(), gi() are the activation functions; Ii(t), Jj(t) show the external inputs at time t.

    In order to establish the existence of periodic solutions of systems (2.1) and (2.2), we assume the following hypotheses:

    (H1) τij(t), ˜τij(t), σji(t), ˜σji(t), aij(t), bij(t), cji(t), dji(t) are continuous ωperiodic functions and

    aMij=max0tωaij(t),bMij=max0tωbij(t),cMji=max0tωcji(t),dMji=max0tωdji(t).

    (H2) fj(x), gi(y) are bounded and globally Lipschitz continuous, i.e., there exist positive constants Fj, Gi, ˜Fj, ˜Gi, such that

    |fj(x)fj(y)|Fj|xy|,|fj(x)|˜Fj,j=1,2,,m,
    |gi(x)gi(y)|Gi|xy|,|gi(y)|˜Gi,i=1,2,,n.

    It is easy to see that

    |fj(x)|Fj|x|+|fj(0)|,|gi(x)|Gi|x|+|gi(0)|,fory=0.

    (H3) ai(u), bi(u), cj(u), dj(u)C(R,R), and there exist positive constants aLi, aMi, cLj, cMj, bLi, bMi, dLj, dMj, such that

    0<aLiai(u)aMi,0<bLi|u|bi(u)bMi|u|,i=1,2,,n,
    0<cLjcj(u)cMj,0<dLj|u|dj(u)dMj|u|,j=1,2,,m.

    (H4) for all x,yR, there exists a positive integer p such that

    tk+p=tk+ω,Ii(k+p)(x)=Iik(x),Jj(k+p)(y)=Jjp(y).

    (H5) Iik() and Jjk() are bounded and Lipschitz continuous functions, that is, there exist constants si, rj, sik and rjk such that

    |Iik(x)Iik(y)|sik|xy|,|Iik()|<si,i=1,2,,n,
    |Jjk(x)Jjk(y)|rjk|xy|,|Jjk()|<rj,j=1,2,,m.

    It is easy to see that

    |Iik(x)|sik|x|+|Iik(0)|,|Jjk(x)|rjk|x|+|Jjk(0)|,fory=0.

    Before presenting our results on the existence and stability of periodic solutions of systems (2.1) and (2.2), we briefly introduce the Mawhin coincidence degree theorem [34].

    Let X and Y be two Banach spaces, L:DomLXY be a linear mapping and N:XY be a continuous mapping. The mapping L is called a Fredholm mapping of index zero if dimKerL=codimImL<+ and ImL is closed in Y. If L is a Fredholm mapping of index zero, there exist continuous projectors P:XX and Q:YY such that ImP=KerL, KerQ=ImL=Im(IQ), then the restriction Lp of L to DomLKerP is invertible. We denote the inverse of that mapping by Kp. If Ω is an open bounded subset of X, the mapping N is said to be Lcompact on ˉΩ if QN(ˉΩ) is bounded and Kp(IQ)N:ˉΩX is compact. Since ImQ is isomorphic to KerL, there exists an isomorphism J:ImQKerL.

    Lemma 1. Let X and Y be two Banach spaces, L:DomLXY be a Fredholm mapping with index zero, ΩX be an open bounded set and N:ˉΩY be Lcompact on ˉΩ. Assume that:

    (a) for each λ(0,1) and xΩDomL, LxλNx,

    (b) for each xΩKerL, QNx0,

    (c) deg{JQN,ΩKerL,0}0. Then equation Lx=λNx has at least one solution in ˉΩDomL.

    In this section, we study the existence of periodic solutions of systems (2.1) and (2.2) based on Mawhin coincidence degree theorem.

    Let

    z(t)=(xT(t),yT(t))T=(x1(t),,xn(t),y1(t),,ym(t))T,

    obviously if z(t) is a solution of systems (2.1) and (2.2) defined on [0,ω] such that z(0)=z(t), then according to the periodicity of systems (2.1) and (2.2) in t, the function z(t) defined by

    z(t)=z(tkω),t[lω,(l+1)ω]{tk},k=1,2,, l=1,2.

    in which z(t) is left continuous at t=tk. Thus z(t) is an ωperiodic solution of systems (2.1) and (2.2).

    For any non-negative integer q, let Cq[0,ω:t1,,tp]={z:[0,ω]Rn+mz(q)(t) exists for tt1,,tp;z(q)(t+) and z(q)(t)exists at t1,,tp and z(j)(tk)=z(j)(tk),k=1,,p, j=1,,q}.

    In order to establish the existence of ωperiodic solutions of systems (2.1) and (2.2), we take

    X={zC[0,ω:t1,,tp]|z(t)=z(t+ω)},Y=X×R(n+m)×(p+1),

    and

    z=n+mi=1max0tω|zi(t)|=ni=1max0tω|xi(t)|+mj=1max0tω|yj(t)|,

    then X and Y are both Banach space.

    Set

    L:DomLXY,z(˙z(t),Δz(t1),Δz(t2),,Δz(tp),0), (3.1)

    where DomL={zC1[0,ω:t1,,tp],z(t)=z(t+ω)}.

    From N:XY, we have

    Nz=((A1(t)An(t)B1(t)Bm(t)),(Δx1(t1)Δxn(t1)Δy1(t1)Δym(t1)),(Δx1(t2)Δxn(t2)Δy1(t2)Δym(t2)),,(Δx1(tp)Δxn(tp)Δy1(tp)Δym(tp)),(0000),), (3.2)

    where

    Ai(t)=ai(xi(t))[bi(xi(t))mj=1aij(t)fj(yj(tτij(t)))mj=1bij(t)fj(˙yj(t˜τij(t)))Ii(t)],

    and

    Bj(t)=cj(yj(t))[dj(yj(t))ni=1cji(t)gi(xi(tσji(t)))ni=1dji(t)gi(˙xi(t˜σji(t)))Jj(t)].

    Obviously,

    KerL=Rn+m,

    and

    ImL={(h,C1,C2,,Cp,d)Y:ω0h(s)ds+pk=1Ck+d=0}=X×R(n+m)×p×{0},

    thus

    dimKerL=codimImL=n+m.

    It is easy to show that ImL is closed in Y and L is a Fredholm mapping of index zero.

    Lemma 2. Let L and N are two mappings defined by (3.1) and (3.2), then N is Lcompact on ˉΩ for any open bounded set ΩX..

    Proof. Define two projectors:

    Pz=1ωω0z(t)dt,

    and

    Qz=Q(h,C1,C2,,Cp,d)=(1ω[ω0h(s)ds+pk=1Ck+d],0,,0,0).

    It is obvious that P and Q are continuous and satisfy

    ImP=KerL and ImL=KerQ=Im(IQ).

    Furthermore, the generalized inverse Kp=L1p is given by

    Kpz=t0h(s)ds+t>tkCk1ωω0t0h(s)dsdtpk=1Ck.

    Then the expression of QNz is

    QNz=((1ωω0Ai(s)ds+1ωpk=1Iik(xi(tk)))n×1(1ωω0Bj(s)ds+1ωpk=1Jjk(yj(tk)))m×1,0,,0,0),

    and

    Kp(IQ)Nz=((t0Ai(s)ds+t>tkIik(xi(tk)))n×1(t0Bj(s)ds+t>tkJjk(yj(tk)))m×1)((1ωω0t0Ai(s)dsdt+(tω12)ω0Ai(s)ds)n×1(1ωω0t0Bj(s)dsdt+(tω12)ω0Bj(s)ds)m×1)((pk=1Iik(xi(tk)))n×1(pk=1Jjk(yj(tk)))m×1).

    Thus QN and Kp(IQ)N are both continuous.

    Consider the sequence {Kp(IQ)N}, for any open bounded set ΩX and any zΩ, we have

    Kp(IQ)Nz=max0tωni=1|t0Ai(s)ds+t>tkIik(xi(tk))1ωω0t0Ai(s)dsdt(tω12)ω0Ai(s)dspk=1Iik(xi(tk))|+max0tωmj=1|t0Bj(s)ds+t>tkJjk(yj(tk))1ωω0t0Bj(s)dsdt(tω12)ω0Bj(s)dspk=1Jjk(yj(tk))|ni=152ω0|Ai(s)|ds+2psi+mj=152ω0|Bj(s)|ds+2prj,

    then Kp(IQ)N is uniformly bounded on Ω. For any z,˜zΩ, we have

    Kp(IQ)NzKp(IQ)N˜z=max0tωni=1|t>tk(Iik(xi(tk))Iik(˜xi(tk)))pk=1(Iik(xi(tk))Iik(˜xi(tk)))|+max0tωmj=1|t>tk(Jjk(yj(tk))Jjk(˜yj(tk)))pk=1(Jjk(yj(tk))Jjk(˜yj(tk)))|ni=1pk=1|(Iik(xi(tk))Iik(˜xi(tk)))|+mj=1pk=1|(Jjk(yj(tk))Jjk(˜yj(tk)))|ni=1pk=1sik|xi(tk)˜xi(tk)|+mi=1pk=1rjk|yj(tk)˜yj(tk)|,

    then Kp(IQ)N is equicontinuous on Ω. By virtue of the Arzela-Ascoli Theorem, Kp(IQ)N(Ω) is a sequentially compact set. Therefore, Kp(IQ)N(ˉΩ) is compact. Moreover, QN(ˉΩ) is bounded. Thus, N is Lcompact on ˉΩ for any open bounded set ΩX.

    Now, we need to show that there exists a domain Ω that satisfies all the requirements given in Lemma 1.

    Theorem 1. Assume that (H1)-(H5) hold, systems (2.1) and (2.2) have at least one ωperiodic solution.

    Proof. Corresponding to the operator equation Lz=λNz, λ(0,1), we have

    {˙xi(t)=λai(xi(t))[bi(xi(t))mj=1aij(t)fj(yj(tτij(t)))mj=1bij(t)fj(˙yj(t˜τij(t)))Ii(t)],t>0,ttk,Δxi(tk)=xi(t+k)xi(tk)=λIik(xi(tk)),i=1,2,,n,k=1,2,,˙yj(t)=λcj(yj(t))[dj(yj(t))ni=1cji(t)gi(xi(tσji(t)))ni=1dji(t)gi(˙xi(t˜σji(t)))Jj(t)],t>0,ttk,Δyj(tk)=yj(t+k)yj(tk)=λJjk(yj(tk)),j=1,2,,m,k=1,2,. (3.3)

    Suppose that (x1(t),,xn(t),y1(t),,ym(t))TX is a solution of system (3.3) for some λ(0,1). Integrating system (3.3) over the interval [0,ω], we obtain

    {ω0{ai(xi(s))[bi(xi(s))mj=1aij(t)fj(yj(sτij(s)))mj=1bij(t)fj(˙yj(s˜τij(s)))Ii(s)]}ds+pk=1Iik(xi(tk))=0,ω0{cj(yj(s))[dj(yj(s))ni=1cji(t)gi(xi(sσji(s)))ni=1dji(t)gi(˙xi(s˜σji(s)))Jj(s)]}ds+pk=1Jjk(yj(tk))=0. (3.4)

    Let ξ, η[0,ω], and

    xi(ξ)=inf0tωxi(t), i=1,2,,n;yj(η)=inf0tωyj(t), j=1,2,,m.

    From (3.4), we have

    xi(ξ)aLibLiωω0ai(xi(s))bi(xi(s))ds=ω0{ai(xi(s))[mj=1aij(t)fj(yj(sτij(s)))mj=1bij(t)fj(˙yj(s˜τij(s)))Ii(s)]}ds+pk=1Iikxi(tk)ω0|ai(xi(s))[mj=1aij(t)fj(yj(sτij(s)))mj=1bij(t)fj(˙yj(s˜τij(s)))Ii(s)]|ds+|pk=1Iikxi(tk)|aMiωmj=1(aMij+bMij)˜Fj+pk=1Iikxi(tk),

    that is,

    xi(ξ)aMiωmj=1(aMij+bMij)˜Fj+pk=1Iikxi(tk)aLibLiω:=T1i.

    Similarly we obtain

    yj(η)cMjωni=1(cMji+dMji)˜Gi+pk=1Jjkyj(tk)cLjdLjω:=T2j.

    Again set ξ+, η+[0,ω], and

    xi(ξ+)=sup0tωxi(t), i=1,2,,n;yj(η+)=sup0tωyj(t), j=1,2,,m,

    obviously

    xi(ξ+)aMiωmj=1(aMij+bMij)˜Fj+pk=1Iikxi(tk)aMibMiω:=T+1i.
    yj(η+)cMjωni=1(cMji+dMji)˜Gi+pk=1Jjkyj(tk)cMjdMjω:=T+2j.

    Denote Hi=max0tω|zi(t)|<max{|T+1i|,|T1i|}, and

    H=n+mi=1Hi+E,

    where E is a sufficiently large positive constant. It is obvious that H is independent of λ. Let

    Ω={z(t)=(xT(t),yT(t))TX:z(t)<H, z(t+k)Ω, k=1,2,p}.

    where x(t)=(x1(t),,xn(t))T, y(t)=(y1(t),,ym(t))T.

    Next we check the three conditions in Lemma 1.

    (a) For each λ(0,1), z(t)ΩDomL with the norm z(t)=H, we have LzλNz.

    (b) For any zΩRn+m, z is a constant vector in Rn+m, z=H, then QNz0.

    (c) Let J:ImQKerL, then

    JQNz=QNz=((1ωω0Ai(s)ds+1ωpk=1Iik(xi(tk)))n×1(1ωω0Bj(s)ds+1ωpk=1Jjk(yj(tk)))m×1,0,,0,0).

    Define Ψ:KerL×[0,1]X by

    Ψ(z,μ)=μz+(1μ)QNz.

    It is easy to verify Ψ(z,μ)(0,0,,0) for any zΩKerL.

    Therefore

    \begin{eqnarray*} deg\{JQN, \Omega \cap ker L, (0, 0, . . . , 0)\} & = °\{QNz, \Omega \cap ker L, (0, 0, . . . , 0)\}\\ & = °\{-z,\Omega \cap ker L, (0, 0, . . . , 0)\}\\ &\neq & 0. \end{eqnarray*}

    All the conditions in Lemma 1 have been verified. We conclude that Lz = Nz has at least one \omega- periodic solution. This implies that systems (2.1) and (2.2) have at least one \omega- periodic solution.

    Remark 1. The method of the estimation for \Omega is classical and effective. In fact, a new study method is cited in [33], utilizing Lyapunov method to study periodic solutions for neural networks. Hence, we also established Lemma 3 and gave a detailed proof in Appendix.

    In this section, we will construct a new Lyapunov functional to study the global asymptotic stability of periodic solutions of systems (2.1) and (2.2).

    Theorem 2. Assume that (H1)–(H5) hold, and

    (H6) a_{i}(u) and c_{j}(u) are globally Lipschitz continuous, that is, for any (u, v)\in \mathbb{R} , there exist constants h_{i}^{a} and h_{j}^{c} such that

    |a_{i}(u)-a_{i}(v)|\leq h_{i}^{a}|u-v|,\quad i = 1,2,\cdots,n,
    |c_{j}(u)-c_{j}(v)|\leq h_{j}^{c}|u-v|,\quad j = 1,2,\cdots,m.

    (H7) for all i, \ j\ (i = 1, 2, \cdots, n, j = 1, 2, \cdots, m), there exist constants L_{i}^{ab} and L_{j}^{cd} such that

    |a_{i}(u)b_{i}(u)-a_{i}(v)b_{i}(v)|\geq L_{i}^{ab}|u-v|,\quad | c_{j}(u)d_{j}(u)-c_{j}(v)d_{j}(v)|\geq L_{j}^{cd}|u-v|,\quad \forall(u,v)\in \mathbb{R}.

    (H8) there exist positive constants \theta_{i}, \ \theta_{n+j} and \gamma \in (0, \min(\Lambda_{1i}, \Lambda_{2j})) such that

    -\theta_{i}(\Lambda_{1i}-\gamma)+\sum\limits_{j = 1}^{m}\theta_{n+j} {c_{j}^{M}}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}e^{\gamma\sigma_{ji}^{\prime M}} < 0,

    and

    -\theta_{n+j}(\Lambda_{2j}-\gamma)+\sum\limits_{i = 1}^{n}\theta_{i} {a_{i}^{M}}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}e^{\gamma\tau_{ij}^{\prime M}} < 0,

    where

    \Lambda_{1i} = L_{i}^{ab}-h_{i}^{a}\sum\limits_{j = 1}^{m}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}-h_{i}^{a}I_{i}^{M},
    \Lambda_{2j} = L_{j}^{cd}-h_{j}^{c}\sum\limits_{i = 1}^{n}(c_{ji}^{M}+d_{ji}^{M})\tilde{G}_{i}-h_{j}^{c}J_{j}^{M},

    in which

    I_{i}^{M} = \max\limits_{0\leq t\leq \omega}I_{i}(t), \quad J_{j}^{M} = \max\limits_{0\leq t\leq \omega}J_{j}(t), \quad \tau_{ij}^{\prime M} = \max\limits_{0\leq t\leq \omega}\tau_{ij}^{\prime}(t),
    \sigma_{ji}^{\prime M} = \max\limits_{0\leq t\leq \omega}\sigma_{ji}^{\prime}(t), \quad \tilde{\tau}_{ij}^{\prime M} = \max\limits_{0\leq t\leq \omega}\tau_{ij}^{\prime}(t), \quad \tilde{\sigma}_{ji}^{\prime M} = \max\limits_{0\leq t\leq \omega}\sigma_{ji}^{\prime}(t),.

    and 1-\tau_{ij}^{\prime M} > 0, \ 1-\sigma_{ji}^{\prime M} > 0, \ 1-\tilde{\tau}_{ij}^{\prime M} > 0, 1-\tilde{\sigma}_{ji}^{\prime M} > 0.

    (H9) -2 < I_{ik} < 0, \ -2 < J_{jk} < 0, \ i = 1, 2, \cdots, n, \ j = 1, 2, \cdots, m, \ k = 1, 2, \cdots, p. Then the \omega- periodic solution (x^{*T}(t), y^{*T}(t))^{T} of systems (2.1) and (2.2) is global asymptotic stable.

    Proof. From Theorem 1, we find that systems (2.1) and (2.2) has at least one \omega- periodic solution under assumptions (H1)-(H5). Let (x^{*T}(t), y^{*T}(t))^{T} be one \omega- periodic solution of systems (2.1) and (2.2). For any solution (x(t)^{T}, y(t)^{T})^{T} of system (2.1), we let

    u_{i}(t) = x_{i}(t)-x_{i}^{*}(t),\quad v_{j}(t) = y_{j}(t)-y_{j}^{*}(t),

    then

    \dot{u}_{i}(t) = \dot{x}_{i}(t)- \dot{x}_{i}^{*}(t),\quad \dot{v}_{j}(t) = \dot{y}_{j}(t)- \dot{y}_{j}^{*}(t).

    It is easy to see that system (2.1) can be reduced to the following system

    \begin{eqnarray} \left\{\begin{array}{ll} \frac{du_{i}(t)}{dt} = -[a_{i}(u_{i}(t)+x_{i}^{*}(t))b_{i}(u_{i}(t)+x_{i}^{*}(t))-a_{i}(x_{i}^{*}(t))b_{i}(x_{i}^{*}(t))] \\ \qquad \quad +a_{i}(u_{i}(t)+x_{i}^{*}(t))\sum\limits_{j = 1}^{m}a_{ij}(t)f_{j}(y_{j}(t-\tau_{ij}(t))) -a_{i}(u_{i}(t)+x_{i}^{*}(t))\sum\limits_{j = 1}^{m}a_{ij}(t)f_{j}(y_{j}^{*}(t-\tau_{ij}(t)))\\ \qquad \quad +a_{i}(u_{i}(t)+x_{i}^{*}(t))\sum\limits_{j = 1}^{m}b_{ij}(t)f_{j}(\dot{y}_{j}(t-\tilde{\tau}_{ij}(t))) -a_{i}(u_{i}(t)+x_{i}^{*}(t))\sum\limits_{j = 1}^{m}b_{ij}(t)f_{j}(\dot{y}_{j}^{*}(t-\tilde{\tau}_{ij}(t)))\\ \qquad \quad +(a_{i}(u_{i}(t)+x_{i}^{*}(t))-a_{i}(x_{i}^{*}(t)))\sum\limits_{j = 1}^{m}a_{ij}(t)f_{j}(y_{j}^{*}(t-\tau_{ij}(t)))\\ \qquad \quad +(a_{i}(u_{i}(t)+x_{i}^{*}(t))-a_{i}(x_{i}^{*}(t)))\sum\limits_{j = 1}^{m}b_{ij}(t)f_{j}(\dot{y}_{j}^{*}(t-\tilde{\tau}_{ij}(t)))\\ \qquad \quad +(a_{i}(u_{i}(t)+x_{i}^{*}(t))-a_{i}(x_{i}^{*}(t)))I_{i}(t),\quad t\in [o,\omega], \quad t\neq t_{k},\\ \Delta x_{i}(t_{k}) = x_{i}(t_{k}^{+})-x_{i}(t_{k}) = I_{ik}(x_{i}(t_{k})), \quad i = 1,2,\cdots,n,\quad k = 1,2,\cdots,\\ \frac{dv_{j}(t)}{dt} = -[c_{j}(v_{j}(t)+y_{j}^{*}(t))d_{j}(v_{j}(t)+y_{j}^{*}(t))-c_{j}(y_{j}^{*}(t))d_{j}(y_{j}^{*}(t))] \\ \qquad \quad +c_{j}(v_{j}(t)+y_{j}^{*}(t))\sum\limits_{i = 1}^{n}c_{ji}(t)g_{i}(x_{i}(t-\sigma_{ji}(t))) -c_{j}(v_{j}(t)+y_{j}^{*}(t))\sum\limits_{i = 1}^{n}c_{ji}(t)g_{i}(x_{i}^{*}(t-\sigma_{ji}(t)))\\ \qquad \quad +c_{j}(v_{j}(t)+y_{j}^{*}(t))\sum\limits_{i = 1}^{n}d_{ji}(t)g_{i}(\dot{x}_{i}(t-\tilde{\sigma}_{ji}(t))) -c_{j}(v_{j}(t)+y_{j}^{*}(t))\sum\limits_{i = 1}^{n}d_{ji}(t)g_{i}(\dot{x}_{i}^{*}(t-\tilde{\sigma}_{ji}(t)))\\ \qquad \quad +(c_{j}(v_{j}(t)+y_{j}^{*}(t))-c_{j}(y_{j}^{*}(t)))\sum\limits_{i = 1}^{n}c_{ji}(t)g_{i}(x_{i}^{*}(t-\sigma_{ji}(t)))\\ \qquad \quad +(c_{j}(v_{j}(t)+y_{j}^{*}(t))-c_{j}(y_{j}^{*}(t)))\sum\limits_{i = 1}^{n}d_{ji}(t)g_{i}(\dot{x}_{i}^{*}(t-\tilde{\sigma}_{ji}(t)))\\ \qquad \quad +(c_{j}(v_{j}(t)+y_{j}^{*}(t))-c_{j}(y_{j}^{*}(t)))J_{j}(t),\quad t\in [o,\omega], \quad t\neq t_{k},\\ \Delta y_{j}(t_{k}) = y_{j}(t_{k}^{+})-y_{j}(t_{k}) = J_{jk}(y_{j}(t_{k})), \quad j = 1,2,\cdots,m,\quad k = 1,2,\cdots. \end{array} \right. \end{eqnarray} (4.1)

    From system (4.1), we have

    \begin{eqnarray} D^{+}{u}_{i}(t) &\leq& -[L_{i}^{ab}-h_{i}^{a}\sum\limits_{j = 1}^{m}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}-h_{i}^{a}I_{i}^{M}]|{u}_{i}(t)| +{a_{i}^{M}}\sum\limits_{j = 1}^{m}a_{ij}^{M}F_{j}(v_{j}(t-\tau_{ij}(t)))\\ & &+{a_{i}^{M}}\sum\limits_{j = 1}^{m}b_{ij}^{M}F_{j}(\dot{v}_{j}(t-\tilde{\tau}_{ij}(t))), \end{eqnarray} (4.2)

    and

    \begin{eqnarray} D^{+}{v}_{j}(t) &\leq& -[L_{j}^{cd}-h_{j}^{c}\sum\limits_{i = 1}^{n}(c_{ji}^{M}+d_{ji}^{M})\tilde{G}_{i}-h_{j}^{c}J_{j}^{M}]|{v}_{j}(t)| +{c_{j}^{M}}\sum\limits_{i = 1}^{n}c_{ji}^{M}G_{i}(u_{i}(t-\sigma_{ji}(t)))\\ & &+{c_{j}^{M}}\sum\limits_{i = 1}^{n}d_{ji}^{M}G_{i}(\dot{u}_{i}(t-\tilde{\sigma}_{ji}(t))), \end{eqnarray} (4.3)

    for all i = 1, 2, \cdots, n \ \mbox{and} \ j = 1, 2, \cdots, m.

    Define V(t) = V_{1}(t) + V_{2}(t) , where

    \begin{eqnarray} V_{1}(t) & = &\sum\limits_{i = 1}^{n}\left[\theta_{i}e^{\gamma t}|{u}_{i}(t)| + {a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}\int_{t- \tau_{ij}(t)}^{t}|v_{j}(s)|e^{\gamma(s+\tau_{ij}^{\prime M})}ds\right.\\ & &\left.+ {a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{b_{ij}^{M}F_{j}}{1- \tilde{\tau}_{ij}^{\prime M}}\int_{t- \tilde{\tau}_{ij}(t)}^{+\infty}|\dot{v}_{j}(s)|e^{\gamma(s+\tilde{\tau}_{ij}^{\prime M})}ds\right], \end{eqnarray} (4.4)

    and

    \begin{eqnarray} V_{2}(t) & = &\sum\limits_{j = 1}^{m}\left[\theta_{n+j} e^{\gamma t}|v_{j}(t)| + {c_{j}^{M}}\sum\limits_{i = 1}^{n}\theta_{n+j}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}\int_{t- \sigma_{ji}(t)}^{t}|u_{i}(s)|e^{\gamma(s+ \sigma_{ji}^{\prime M})}ds\right.\\ & &\left.+ {c_{j}^{M}}\sum\limits_{i = 1}^{n}\theta_{n+j}\frac{d_{ji}^{M} G_{i}}{1- \tilde{\sigma}_{ji}^{\prime M}}\int_{t- \tilde{\sigma}_{ji}(t)}^{+\infty}|\dot{u}_{i}(s)|e^{\gamma(s+ \tilde{\sigma}_{ji}^{\prime M})}ds\right], \end{eqnarray} (4.5)

    From (4.2) and (4.3), we have

    \begin{eqnarray*} &&D^{+}{V}_{1}(t) \leq \sum\limits_{i = 1}^{n}\theta_{i}\big(-(L_{i}^{ab}-h_{i}^{a}\sum\limits_{j = 1}^{m}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}-h_{i}^{a}I_{i}^{M}-\gamma)\big) |{u}_{i}(t)|e^{\gamma t}\\ & &+\theta_{i}e^{\gamma t}{a_{i}^{M}}\sum\limits_{j = 1}^{m}a_{ij}^{M}F_{j}(v_{j}(t-\tau_{ij}(t))) +\theta_{i}e^{\gamma t}{a_{i}^{M}}\sum\limits_{j = 1}^{m}b_{ij}^{M}F_{j}(\dot{v}_{j}(t-\tilde{\tau}_{ij}(t)))\\ & &+ {a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}} \big[|v_{j}(t)|e^{\gamma(t+\tau_{ij}^{\prime M})}-|v_{j}(t- \tau_{ij}(t))|e^{\gamma(t- \tau_{ij}(t)+\tau_{ij}^{\prime M})}(1-\tau_{ij}^{\prime}(t))\big]\\ & &+ {a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{b_{ij}^{M}F_{j}}{1- \tilde{\tau}_{ij}^{\prime M}} \big[-|\dot{v}_{j}(t- \tilde{\tau}_{ij}(t))|e^{\gamma(t-\tilde{\tau}_{ij}(t)+\tilde{\tau}_{ij}^{\prime M})}(1-\tilde{\tau}_{ij}^{\prime}(t))\big]. \end{eqnarray*}

    According to

    1-\tau_{ij}^{\prime}(t)\geq 1-\tau_{ij}^{\prime M},\quad e^{\gamma(t- \tau_{ij}(t)+\tau_{ij}^{\prime M})}\geq e^{\gamma t},
    1-\tilde{\tau}_{ij}^{\prime}(t)\geq 1-\tilde{\tau}_{ij}^{\prime M},\quad e^{\gamma(t-\tilde{\tau}_{ij}(t)+\tilde{\tau}_{ij}^{\prime M})}\geq e^{\gamma t},

    we obtain

    \begin{eqnarray*} D^{+}{V}_{1}(t) \leq \sum\limits_{i = 1}^{n}\left[-\theta_{i}(\Lambda_{1i}-\gamma)|{u}_{i}(t)|e^{\gamma t}+\sum\limits_{j = 1}^{m}\theta_{i} {a_{i}^{M}}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}|v_{j}(t)|e^{\gamma(t+\tau_{ij}^{\prime M})}\right], \end{eqnarray*}

    where

    \Lambda_{1i} = L_{i}^{ab}-h_{i}^{a}\sum\limits_{j = 1}^{m}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}-h_{i}^{a}I_{i}^{M}.

    Similarly to the calculation of D^{+}{V}_{1}(t) , we have

    \begin{eqnarray*} D^{+}{V}_{2}(t)\leq \sum\limits_{j = 1}^{m}\left[-\theta_{n+j}(\Lambda_{2i}-\gamma)|{v}_{j}(t)|e^{\gamma t}+\sum\limits_{i = 1}^{n}\theta_{n+j} {c_{j}^{M}}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}|u_{i}(t)|e^{\gamma(t+\sigma_{ji}^{\prime M})} \right], \end{eqnarray*}

    Hence

    \begin{eqnarray*} D^{+}{V}(t)&\leq& e^{\gamma t}\sum\limits_{i = 1}^{n}\left[-\theta_{i}(\Lambda_{1i}-\gamma)+\sum\limits_{j = 1}^{m}\theta_{n+j} {c_{j}^{M}}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}e^{\gamma\sigma_{ji}^{\prime M}}\right]|{u}_{i}(t)|\\ & &+e^{\gamma t}\sum\limits_{j = 1}^{m}\left[-\theta_{n+j}(\Lambda_{2i}-\gamma)+\sum\limits_{i = 1}^{n}\theta_{i} {a_{i}^{M}}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}e^{\gamma\tau_{ij}^{\prime M}}\right]|{v}_{j}(t)|\\ & < &0. \end{eqnarray*}

    In view of assumption (H3), we have

    \begin{eqnarray*} V(t_{k}^{+})& = & \sum\limits_{i = 1}^{n}\left[\theta_{i} e^{\gamma t_{k}^{+}}|{u}_{i}(t_{k}^{+})|+{a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}\int_{t_{k}^{+}- \tau_{ij}(t_{k}^{+})}^{t_{k}^{+}}|v_{j}(s)|e^{\gamma(s+\tau_{ij}^{\prime M})}ds\right.\\ & &+\left.\sum\limits_{j = 1}^{m}\theta_{i}{a_{i}^{M}}\frac{b_{ij}^{M}F_{j}}{1- \tilde{\tau}_{ij}^{\prime M}}\int_{t_{k}^{+}- \tilde{\tau}_{ij}(t_{k}^{+})}^{+\infty}|\dot{v}_{j}(s)|e^{\gamma(s+\tilde{\tau}_{ij}^{\prime M})}ds \right]\\ & &+\sum\limits_{j = 1}^{m}\left[\theta_{n+j} e^{\gamma t_{k}^{+}}|v_{j}(t_{k}^{+})|+ {c_{j}^{M}}\sum\limits_{i = 1}^{n}\theta_{n+j}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}\int_{t_{k}^{+}- \sigma_{ji}(t_{k}^{+})}^{t_{k}^{+}}|u_{i}(s)|e^{\gamma(s+ \sigma_{ji}^{\prime M})}ds\right.\\ & &\left.+\sum\limits_{j = 1}^{m}\sum\limits_{i = 1}^{n}\theta_{n+j}{c_{j}^{M}}\frac{d_{ji}^{M} G_{i}}{1- \tilde{\sigma}_{ji}^{\prime M}}\int_{t_{k}^{+}- \tilde{\sigma}_{ji}(t_{k}^{+})}^{+\infty}|\dot{u}_{i}(s)|e^{\gamma(s+ \tilde{\sigma}_{ji}^{\prime M})}ds\right]\\ &\leq& \sum\limits_{i = 1}^{n}\left[\theta_{i} e^{\gamma t_{k}}|(1+I_{ik}){u}_{i}(t_{k})|+ {a_{i}^{M}}\sum\limits_{j = 1}^{m}\theta_{i}\frac{a_{ij}^{M}F_{j}}{1- \tau_{ij}^{\prime M}}\int_{t_{k}- \tau_{ij}(t_{k})}^{t_{k}}|v_{j}(s)|e^{\gamma(s+\tau_{ij}^{\prime M})}ds\right.\\ & &+\left.\sum\limits_{j = 1}^{m}\theta_{i}{a_{i}^{M}}\frac{b_{ij}^{M}F_{j}}{1- \tilde{\tau}_{ij}^{\prime M}}\int_{t_{k}- \tilde{\tau}_{ij}(t_{k})}^{+\infty}|\dot{v}_{j}(s)|e^{\gamma(s+\tilde{\tau}_{ij}^{\prime M})}ds\right]\\ & &+\sum\limits_{j = 1}^{m}\left[\theta_{n+j} e^{\gamma t_{k}}|(1+J_{jk})v_{j}(t_{k})|+ {c_{j}^{M}}\sum\limits_{i = 1}^{n}\theta_{n+j}\frac{c_{ji}^{M}G_{i}}{1- \sigma_{ji}^{\prime M}}\int_{t_{k}- \sigma_{ji}(t_{k})}^{t_{k}}|u_{i}(s)|e^{\gamma(s+ \sigma_{ji}^{\prime M})}ds \right.\\ & &\left.+\sum\limits_{j = 1}^{m}\sum\limits_{i = 1}^{n}\theta_{n+j}{c_{j}^{M}}\frac{d_{ji}^{M} G_{i}}{1- \tilde{\sigma}_{ji}^{\prime M}}\int_{t_{k}- \tilde{\sigma}_{ji}(t_{k})}^{+\infty}|\dot{u}_{i}(s)|e^{\gamma(s+ \tilde{\sigma}_{ji}^{\prime M})}ds\right]\\ &\leq& V(t_{k}). \end{eqnarray*}

    Thus, by the standard Lyapunov functional theory, the periodic solution (x^{*T}, y^{*T})^{T} is global asymptotic stable. The proof is complete.

    Remark 2. In [20], the authors discussed a model describing dynamics of delayed Cohen-Grossberg-type BAM neural networks without impulses. In our paper, we not only consider the impact of impulses and construct a new Lyapunov functional to establish the existence and the global asymptotic stability of periodic solutions.

    Remark 3. In [16], the impulsive function is linear. We discussed a kind of more general bounded and Lipschitz continuous functions in this paper.

    Remark 4. In [32], the authors discussed a kind of BAM neural networks with time-varying delays and impulses. In this paper, we propose a new kind of Cohen-Grossberg-type BAM neural network systems, which have a more wide application in practice. Obviously Cohen-Grossberg-type BAM neural networks can be reduced to Hopfield-type BAM neural networks and cellular BAM neural networks.

    Corollary 1. Suppose that the assumptions (H1)–(H3) hold, then the systems (2.1) and (2.2) without impulses has at least one \omega- periodic solution.

    Remark 5. The consideration of neutral delays may affect the stability of systems since the presence of delays may induce complex behaviors for the schemes. The existence of delays plays an increasingly important role in many disciplines like economic, mathematics, science, and engineering, which can also help describe propagation and transport phenomena or population dynamics, etc. For instance, in economic systems, presence delays appear in a natural way since decisions and effects are separated by some time interval.

    As applications, we present an example to illustrate our main results in Theorem 1 and Theorem 2.

    Example 1. Consider the Cohen-Grossberg-type BAM neural networks with m = n = 1 , which is given as

    \begin{eqnarray} \left\{\begin{array}{ll} \dot{x}_{1}(t) = -a_{1}(x_{1}(t))\left[b_{1}(x_{1}(t))- a_{11}(t)f_{1}(y_{1}(t-\tau_{11}(t)))- b_{11}(t)f_{1}(\dot{y}_{1}(t-\tilde{\tau}_{11}(t)))-I_{1}(t)\right], t > 0,t\neq t_{k}, \\ \Delta x_{1}(t_{k}) = I_{1k}(x_{1}(t_{k})), \quad k = 1,2,\cdots, \\ \dot{y}_{1}(t) = -c_{1}(y_{1}(t))\left[d_{1}(y_{1}(t))-c_{11}(t)g_{1}(x_{1}(t-\sigma_{11}(t)))-d_{11}(t)g_{1}(\dot{x}_{1}(t-\tilde{\sigma}_{11}(t)))-J_{1}(t)\right], t > 0,t\neq t_{k}, \\ \Delta y_{1}(t_{k}) = J_{1k}(y_{1}(t_{k})),\quad k = 1,2,\cdots, \end{array} \right. \end{eqnarray} (5.1)

    where

    a_{1}(u) = \frac{1}{3}+\frac{1}{3}sinu, \quad b_{1}(u) = u(\frac{1}{3}-\frac{1}{3}sinu), \quad c_{1}(u) = \frac{1}{2}+\frac{1}{2}cosu, \quad d_{1}(u) = u(\frac{1}{2}-\frac{1}{2}cosu),
    f_{1}(u) = sinu, \quad g_{1}(u) = \frac{1}{2}(|sinu+1|-|sinu-1|), \quad I_{1}(t) = \frac{1}{15}(-2+sint),
    J_{1}(t) = \frac{1}{15}(-2+cost),\quad a_{11}(t) = b_{11}(t) = c_{11}(t) = d_{11}(t) = \frac{1}{30}sint,
    \tau_{11} = \tilde{\tau}_{11} = \sigma_{11} = \tilde{\sigma}_{11} = \frac{1}{10}sint,\quad I_{1k}(u) = -1+sinu,\quad J_{1k}(u) = -1+cosu.

    By a straightforward calculation, we obtain

    a_{11}^{M} = b_{11}^{M} = c_{11}^{M} = d_{11}^{M} = \frac{1}{30}, \quad \tilde{F}_{1} = 1,\quad \tilde{G}_{1} = 1,\quad I_{1}^{M} = J_{1}^{M} = -\frac{1}{15},
    h_{1}^{a} = \frac{1}{3},\quad h_{1}^{c} = \frac{1}{2},\quad a_{1}^{L} = c_{1}^{L} = 0, \quad a_{1}^{M} = c_{1}^{M} = \frac{2}{3}, \quad{b}_{1}^{L} = d_{1}^{L} = 0,
    {b}_{1}^{M} = d_{j}^{M} = 1,\quad L_{1}^{ab} = \frac{1}{9},\quad L_{1}^{cd} = \frac{1}{4},\quad \tau_{11}^{\prime M} = \tilde{\tau}_{11}^{\prime M} = \sigma_{11}^{\prime M} = \tilde{\sigma}_{11}^{\prime M} = \frac{1}{10}.

    Thus

    \Lambda_{11} = \frac{4}{45}\ \quad \mbox{and}\ \quad \Lambda_{21} = \frac{13}{60}.

    Choose \gamma = \frac{1}{100} \in \left(0, \min(\Lambda_{11}, \Lambda_{21})\right) , there exist positive constants \theta_{1} = 100 and \theta_{2} = 50 , such that

    \begin{eqnarray*} -\theta_{1}(\Lambda_{11}-\gamma)+ \theta_{2} {c_{1}^{M}}\frac{c_{11}^{M}G_{1}}{1- \sigma_{ji}^{\prime M}}e^{\gamma\sigma_{11}^{\prime M}} & = &-100\times(\frac{4}{45}-\frac{1}{100})+\frac{100}{81}\times e^{\frac{1}{1000}}\\ &\approx& -6.653085802263323 < 0, \end{eqnarray*}

    and

    \begin{eqnarray*} -\theta_{2}(\Lambda_{21}-\gamma)+\theta_{1} {a_{1}^{M}}\frac{a_{11}^{M}F_{1}}{1- \tau_{11}^{\prime M}}e^{\gamma\tau_{11}^{\prime M}} & = &-50\times(\frac{4}{45}-\frac{1}{100})+\frac{200}{81}\times e^{\frac{1}{1000}}\\ &\approx&-1.472838271193313 < 0. \end{eqnarray*}

    By Theorem 1, system (5.1) has a 2\pi- periodic solution. From Theorem 2, all other solutions of system (5.1) converges asymptotically to the periodic solution as t\rightarrow+\infty.

    We have introduced Cohen-Grossberg-type BAM neural networks (2.1) and (2.2) with neutral-type time delays and impulses, and obtained some results on the existence and stability of periodic solutions. The impulse terms in (2.1) are bounded and Lipschitz continuous functions instead of linear functions. What's more, we utilized both the method of the classical estimation and Lyapunov functional construction to search for the region of periodic solutions.

    The existing proof of periodic solution is very classical and effective approach and the method has been widely applied to studying the periodic solution for more 25 years. The proof of the global stability part has some novel in constructing Laypunov function. Hence, we also apply the new Lyapunov function [33] as in the stability to entail the proof of the existing part.

    Lemma 3. For any \lambda \in (0, 1) , consider the operator equation Lz = \lambda Nz, if the periodic solutions of system (3.3) exist, then they are bounded and the boundary is independent of the choice of \lambda under assumption (H1)-(H5), Namely, there exists a positive constant \bar{H}_{0} such that when \|(x^T(t), y^T(t))^T\| = \|(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^T\| \leq \bar{H}_{0} .

    Proof. Suppose that (x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^{T} \in X is a solution of system (3.3) for some \lambda \in (0, 1). It can be thus obtained from system (3.3) that

    \begin{eqnarray} D^{+}{x}_{i}(t) &\leq& -\lambda\left[ a_{i}^{L}b_{i}^{L}|x_{i}(t)|-\sum\limits_{j = 1}^{m}a_{i}^{M}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}-a_{i}^{M}I_{i}^{M} \right]\\ &\doteq& -\lambda\left[ \zeta_{11}|x_{i}(t)|-\zeta_{12} \right], \end{eqnarray} (6.1)

    and

    \begin{eqnarray} D^{+}{y}_{j}(t) &\leq& -\lambda\left[ c_{j}^{L}d_{j}^{L}|y_{j}(t)|-\sum\limits_{i = 1}^{n}c_{j}^{M}(c_{ji}^{M}+d_{ji}^{M})\tilde{G}_{i}-c_{j}^{M}J_{j}^{M} \right]\\ &\doteq& -\lambda\left[ \zeta_{21}|x_{i}(t)|-\zeta_{22} \right], \end{eqnarray} (6.2)

    for all i = 1, 2, \cdots, n \ \mbox{and} \ j = 1, 2, \cdots, m, where

    \zeta_{11} = a_{i}^{L}b_{i}^{L},\quad \zeta_{12} = \sum\limits_{j = 1}^{m}a_{i}^{M}(a_{ij}^{M}+b_{ij}^{M})\tilde{F}_{j}+a_{i}^{M}I_{i}^{M},
    \zeta_{21} = c_{j}^{L}d_{j}^{L},\quad \zeta_{22} = \sum\limits_{i = 1}^{n}c_{j}^{M}(c_{ji}^{M}+d_{ji}^{M})\tilde{G}_{i}+c_{j}^{M}J_{j}^{M}.

    Define V(t) = V_{1}(t) + V_{2}(t) , where

    V_{1}(t) = \sum\limits_{i = 1}^{n}\mu_{1i}|x_{i}(t)|^2+\sum\limits_{i = 1}^{n}\mu_{2i}|x_{i}(t)|,

    and

    V_{2}(t) = \sum\limits_{j = 1}^{m}\delta_{1j}|y_{j}(t)|^2+\sum\limits_{j = 1}^{m}\delta_{2j}|y_{j}(t)|,

    in which \mu_{1i}, \mu_{2i}, \delta_{1j}, \delta_{2j} > 0. Since (x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^{T} is a periodic solution of system (3.3), then V(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t)) is a periodic function.

    One may further get

    \begin{eqnarray*} D^{+}{V}_{1}(t) &\leq& -2\lambda\sum\limits_{i = 1}^{n}\mu_{1i}|x_{i}(t)|( \zeta_{11}|x_{i}(t)|-\zeta_{12} )-\lambda\sum\limits_{i = 1}^{n}\mu_{2i}(\zeta_{11}|x_{i}(t)|-\zeta_{12})\\ & = &\sum\limits_{i = 1}^{n}\left[-2\lambda\zeta_{11}\mu_{1i}|x_{i}(t)|^2+(2\lambda\mu_{1i}\zeta_{12}-\lambda\mu_{2i}\zeta_{11})|x_{i}(t)|+\lambda\mu_{2i}\zeta_{12}\right], \end{eqnarray*}

    and

    \begin{eqnarray*} D^{+}{V}_{2}(t) &\leq& -2\lambda\sum\limits_{j = 1}^{m}\delta_{1j}|y_{j}(t)|( \zeta_{21}|y_{j}(t)|-\zeta_{22} ) -\lambda\sum\limits_{j = 1}^{m}\delta_{2j}(\zeta_{21}|y_{j}(t)|-\zeta_{22})\\ & = &\sum\limits_{j = 1}^{m}\left[-2\lambda\zeta_{21}\delta_{1j}|y_{j}(t)|^2+(2\lambda\delta_{1j}\zeta_{22}-\lambda\delta_{2j}\zeta_{21})|y_{j}(t)|+\lambda\delta_{2j}\zeta_{22}\right]. \end{eqnarray*}

    Thus

    \begin{eqnarray*} D^{+}{V}(t) &\leq & \sum\limits_{i = 1}^{n}\left[-2\lambda\zeta_{11}\mu_{1i}|x_{i}(t)|^2+(2\lambda\mu_{1i}\zeta_{12}-\lambda\mu_{2i}\zeta_{11})|x_{i}(t)|+\lambda\mu_{2i}\zeta_{12}\right]\\ &+&\sum\limits_{j = 1}^{m}\left[-2\lambda\zeta_{21}\delta_{1j}|y_{j}(t)|^2+(2\lambda\delta_{1j}\zeta_{22}-\lambda\delta_{2j}\zeta_{21})|y_{j}(t)|+\lambda\delta_{2j}\zeta_{22}\right]. \end{eqnarray*}

    From

    -2\lambda\zeta_{11}\mu_{1i}|x_{i}(t)|^2+(2\lambda\mu_{1i}\zeta_{12}-\lambda\mu_{2i}\zeta_{11})|x_{i}(t)|+\lambda\mu_{2i}\zeta_{12} = 0,

    and

    -2\lambda\zeta_{21}\delta_{1j}|y_{j}(t)|^2+(2\lambda\delta_{1j}\zeta_{22}-\lambda\delta_{2j}\zeta_{21})|y_{j}(t)|+\lambda\delta_{2j}\zeta_{22} = 0,

    there exist positive constants h_{1}, h_{2} such that the solutions satisfy

    \max\limits_{t\in[0,\omega]}|x_{i}(t)| > h_{1},\; \; \; \; \; \max\limits_{t\in[0,\omega]}|y_{j}(t)| > h_{2}.

    It follows that when \|x_{i}(t)\| > h_{1}, \|y_{j}(t)\| > h_{2}, we can choose a positive constant \tilde{H} = nh_{1}+mh_{2}, such that \|(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^{T}\| > \tilde{H}, and

    -2\lambda\zeta_{11}\mu_{1i}|x_{i}(t)|^2+(2\lambda\mu_{1i}\zeta_{12}-\lambda\mu_{2i}\zeta_{11})|x_{i}(t)|+\lambda\mu_{2i}\zeta_{12} < 0,

    and

    -2\lambda\zeta_{21}\delta_{1j}|y_{j}(t)|^2+(2\lambda\delta_{1j}\zeta_{22}-\lambda\delta_{2j}\zeta_{21})|y_{j}(t)|+\lambda\delta_{2j}\zeta_{22} < 0.

    Hence,

    D^{+}{V}(t) < 0.

    In fact, if \|(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^{T}\| is unbounded, then for any \bar{H} > \tilde{H}, we have

    \|(x_{1}(t),\cdots,x_{n}(t), y_{1}(t),\cdots, y_{m}(t))^{T}\| > \bar{H} > \tilde{H}.

    It follows that D^{+}{V}(t) < 0, which contradicts the fact that V(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t)) is a periodic function. This implies that \|(x_{1}(t), \cdots, x_{n}(t), y_{1}(t), \cdots, y_{m}(t))^{T}\| \leq \bar{H}_{0}, where \bar{H}_{0} is a positive constant. This completes the proof.

    The authors wish to express sincere thanks to the anonymous reviewers for their very careful corrections, valuable comments and suggestions for improving the quality of the paper. This work is supported by the Natural Science Foundation of China (Grants No. 11771185 and 11871251) and Natural Science Foundation of Jiangsu Higher Education Institutions (18KJB180027).

    The authors confirm no conflicts of interest in this paper.



    [1] W. G. Faris, Weak Lebesgue spaces and quantum mechanical binding, Duke Math. J., 43 (1976), 365–373. https://doi.org/10.1215/S0012-7094-76-04332-5 doi: 10.1215/S0012-7094-76-04332-5
    [2] A. Benyi, C. T. Oh, Best constants for certain multilinear integral operators, J. Inequal. Appl., 2006, 28582. https://doi.org/10.1155/jia/2006/28582
    [3] T. Batbold, Y. Sawano, Sharp bounds for m-linear Hilbert-type operators on the weighted Morrey spaces, Math. Inequal. Appl., 20 (2017), 263–283. https://doi.org/10.7153/mia-20-20 doi: 10.7153/mia-20-20
    [4] M. Christ, L. Grafakos, Best constants for two nonconvolution inequalities, P. Am. Math. Soc., 123 (1995), 1687–1693. https://doi.org/10.1090/s0002-9939-1995-1239796-6 doi: 10.1090/s0002-9939-1995-1239796-6
    [5] J. Y. Chu, Z. W. Fu, Q. Y. Wu, L^p and BMO bounds for weighted Hardy operators on the Heisenberg group, J. Inequal. Appl., 2016, 1–12. https://doi.org/10.1186/s13660-016-1222-x
    [6] T. Coulhon, D. M\ddot{u}ller, J. Zienkiewicz, About Riesz transforms on the Heisenberg groups, Math. Ann., 305 (1996), 369–379. https://doi.org/10.1007/bf01444227 doi: 10.1007/bf01444227
    [7] Z. W. Fu, L. Grafakos, S. Z. Lu, F. Y. Zhao, Sharp bounds for m-linear Hardy and Hilbert operators, Houston J. Math., 38 (2012), 225–243.
    [8] G. Gao, X. Hu, C. Zhang, Sharp weak estimates for Hardy-type operators, Ann. Funct. Anal., 7 (2016), 421–433. https://doi.org/10.1215/20088752-3605447 doi: 10.1215/20088752-3605447
    [9] G. Gao, F. Y. Zhao, Sharp weak bounds for Hausdorff operators, Anal. Math., 41 (2015), 163–173. https://doi.org/10.1007/s10476-015-0204-4 doi: 10.1007/s10476-015-0204-4
    [10] Q. J. He, M. Q. Wei, D. Y. Yan, Sharp bound for generalized m-linear n-dimensional Hardy-Littlewood-Pólya operator, Anal. Theor. Appl., 37 (2021), 1–14. https://doi.org/10.4208/ata.OA-2020-0039 doi: 10.4208/ata.OA-2020-0039
    [11] G. H. Hardy, Note on a theorem of Hilbert, Math. Z., 6 (1920), 314–317. https://doi.org/10.1007/bf01199965
    [12] A. Korányi, H. Reimann, Quasiconformal mappings on the Heisenberg group, Invent. Math., 80 (1985), 309–338. https://doi.org/10.1007/bf01388609 doi: 10.1007/bf01388609
    [13] S. Z. Lu, D. D. Yan, F. Y. Zhao, Sharp bounds for Hardy type operators on higher-dimensional product spaces, J. Inequal. Appl., 2013 (2013), 1–11. https://doi.org/10.1186/1029-242x-2013-148 doi: 10.1186/1029-242x-2013-148
    [14] S. Lu, Some recent progress of n-dimensional Hardy operators, Adv. Math. China, 42 (2013), 737–747.
    [15] Y. Mizuta, A. Nekvinda, T. Shimomura, Optimal estimates for the fractional Hardy operator, Stud. Math., 227 (2015), 1–19. https://doi.org/10.4064/sm227-1-1 doi: 10.4064/sm227-1-1
    [16] S. Semmes, An introduction to Heisenberg groups in analysis and geometry, Not. Am. Math. Soc., 50 (2003), 173–186.
    [17] S. Stević, Note on norm of an m-linear integral-type operator between weighted-type spaces, Adv. Differ. Equ., 2021 (2021), 1–10. https://doi.org/10.1186/s13662-021-03346-4 doi: 10.1186/s13662-021-03346-4
    [18] S. Thangavelu, Harmonic analysis on the Heisenberg group, MA: Birkhauser Boston, 159 (1998).
    [19] Q. Y. Wu, Z. W. Fu, Sharp estimates of m-linear p-adic Hardy and Hardy-Littlewood-Pólya operators, Appl. Math., 2011, 1–20.
    [20] H. X. Yu, J. F. Li, Sharp weak bounds for n-dimensional fractional Hardy operators, Front. Math. China, 13 (2018), 449–457. https://doi.org/10.1007/s11464-018-0685-0 doi: 10.1007/s11464-018-0685-0
    [21] F. Y. Zhao, Z. W. Fu, S. Z. Lu, Endpoint estimates for n-dimensional Hardy operators and their commutators, Sci. China Math., 55 (2012), 1977–1990. https://doi.org/10.1007/s11425-012-4465-0 doi: 10.1007/s11425-012-4465-0
    [22] F. Y. Zhao, S. Z. Lu, The best bound for n-dimensional fractional Hardy operators, Math. Inequal. Appl., 18 (2015), 233–240. https://doi.org/10.7153/mia-18-17 doi: 10.7153/mia-18-17
    [23] G. Zhang, Q. Li, Q. Wu, The Weighted and estimates for fractional Hausdorff operators on the Heisenberg Group, J. Funct. Space., 2020.
    [24] V. A. Zorich, O. Paniagua, Mathematical analysis II, Berlin: Springer, 2016. https://doi.org/10.5860/choice.42-0997b
    [25] X. S. Zhang, M. Q. Wei, D. Y. Yan, Sharp bound of Hausdorff operators on Morrey spaces with power weights, J. Univ. Chinese Acad. Sci., 38 (2021), 577.
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