一类分布参数动态系统的周期性分析

通过构造适当的Lyapunov泛函、利用M矩阵性质和不等式技巧, 在不要求神经网络激励函数的有界性、单调性和可微性弱保守条件下, 探讨了一类具有分布参数和分布时滞的Cohen-Grossberg动态神经网络周期解的存在性和指数稳定性问题, 提出了一系列充分性判据来确保这类同时具有分布参数和分布时滞神经网络周期解的存在性和指数稳定性, 并通过几个注解以及与其他文献结果进行比较说明了该方法的优越性. 最后, 给出了数值例子和计算机仿真来验证这一理论的有效性.     

Dynamic behaviors of memristor-based delayed recurrent networks

This paper investigates the problem of the existence and global exponential stability of the periodic solution of memristor-based delayed network. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent networks is established. Several sufficient conditions are obtained, which ensure the existence of periodic solutions and global exponential stability of the memristor-based delayed recurrent networks. These results ensure global exponential stability of memristor-based network in the sense of Filippov solutions. And, it is convenient to estimate the exponential convergence rates of this network by the results. An illustrative example is given to show the effectiveness of the theoretical results.     

Exponential stability and periodic oscillatory solution in BAMnetworks with delays

Both exponential stability and periodic oscillatory solution of bidirectional associative memory (BAM) networks with axonal signal transmission delays are considered by constructing suitable Lyapunov functional and some analysis techniques. Some simple sufficient conditions are given ensuring the global exponential stability and the existence of periodic oscillatory solutions of BAM with delays. These conditions are presented in terms of system parameters and have important leading significance in the design and applications of globally exponentially stable and periodic oscillatory neural circuits for BAM with delays. In addition, two examples are given to illustrate the results.     

Global stability analysis of a class of delayed cellular neural networks

Employing Brouwer’s fixed point theorem, matrix theory, a continuation theorem of the coincidence degree and inequality analysis, the authors study further global exponential stability and the existence of periodic solutions of a class of cellular neural networks with delays (DCNNs) in this paper. A family of sufficient conditions is given for checking global exponential stability and the existence of periodic solutions of DCNNs. The results extend and improve the earlier publications.     

Existence and exponential stability of periodic solutions for a class of Cohen–Grossberg neural networks with bounded and unbounded delays

This paper is concerned with existence and global exponential stability of periodic solutions for a class of Cohen–Grossberg neural networks with bounded and unbounded delays. By the continuation theorem of coincidence degree theory and differential inequality techniques, we deduce some sufficient conditions ensuring existence as well as global exponential stability of periodic solution. These conditions in our results are milder and less restrictive than that of previous known criteria since the hypothesis of boundedness and differentiability on the activation function are dropped. The theoretical analysis are verified by numerical simulations.     

On maximum periodic solutions of integrodifferential equations of volterra type and their stability

Consideration was given to the question of asymptotic (exponential) stability of the maximum periodic solutions of the integrodifferential equations which have an asymptotically stable linear part and small periodic (exponential maximum periodic) perturbation. Under the unlimitedly increasing time, these solutions tend to the periodic modes. The sufficient conditions for asymptotic stability were indicated. In the resonance case where the linearized equation has a pair of purely imaginary roots with the corresponding oscillation frequency coinciding with the oscillation frequency of the periodic part of small perturbation (time function) and the coefficients of the power series expansion of the nonlinear terms, consideration was given to the problem of existence for the maximum periodic solutions of the integrodifferential equation. Conditions were established for existence of such solutions representable by the power series in the fractional degrees of the small parameter characterizing the value of small perturbation in the equation.     

Almost periodic cellular neural networks with neutral-type proportional delays

This paper presents a new result on the existence, uniqueness and generalised exponential stability of almost periodic solutions for cellular neural networks with neutral-type proportional delays and D operator. Based on some novel differential inequality techniques, a testable condition is derived to ensure that all the state trajectories of the system converge to an almost periodic solution with a positive exponential convergence rate. The effectiveness of the obtained result is illustrated by a numerical example.     

Pseudo almost periodic solutions of discrete-time neutral-type neural networks with delays

This paper is concerned with discrete-time neutral-type neural networks with delays. The existence and uniqueness results of pseudo almost periodic solutions are established by applying the contraction mapping principal. By using some mathematical analysis techniques, we further obtain the boundness, exponential attractivity and global exponential stability of pseudo almost periodic solutions for the considered networks. Finally, a typical example and the corresponding numerical simulations have been carried out to support our analytic findings.     

Exponential Stability of Pseudo Almost Periodic Solutions for Neutral Type Cellular Neural Networks with <Emphasis Type="Italic">D</Emphasis> Operator

This article is concerned with a class of neutral type cellular neural networks with D operator. By using Lyapunov functional method and differential inequality techniques, we establish a novel result to ensure the existence and global exponential stability of pseudo almost periodic solutions for the addressed system. In addition, an example and its numerical simulations are given to illustrate our result.     

Exponential Stability of Pseudo Almost Periodic Solutions for Cellular Neural Networks with Multi-Proportional Delays

This paper concerns with the pseudo almost periodic solutions for a class of cellular neural networks model with multi-proportional delays. By applying contraction mapping fixed point theorem and differential inequality techniques, we establish some sufficient conditions for the existence and exponential stability of pseudo almost periodic solutions for the model, which improve and supplement existing ones. Moreover, an example and its numerical simulation are given to support the theoretical results.     

The numerical simulation of periodic solutions for a predator–prey system

In this article, the existence and global stability of periodic solutions for a semi-ratio-dependent predator–prey system with Holling IV functional response and time delays are investigated. Using coincidence degree theory and Lyapunov method, sufficient conditions for the existence and global stability of periodic solutions are obtained. A numerical simulation is given to illustrate the results.     

Existence and stability of almost periodic solutions for CNNs with continuously distributed leakage delays

In this paper, a class of nonlinear nonautonomous cellular neural networks system with the introduction of continuously distributed delays in the leakage (or forgetting) terms is considered. By using Lyapunov functional method and differential inequality techniques, without assuming the boundedness on the activation functions, we derive a set of easily verifiable sufficient conditions that guarantee the existence and exponential stability of almost periodic solutions for the system. Moreover, an example and its numerical simulation are given to demonstrate the feasibility of our method.     

Novel Sufficient Conditions on Periodic Solutions for Discrete-Time Neutral-Type Neural Networks

Neural Processing Letters - In this paper, we consider the existence and global exponential stability of periodic solutions for a class of delayed discrete-time neutral-type neural networks. Novel...     

Almost periodic solutions for quaternion-valued shunting inhibitory cellular neural networks of neutral type with time delays in the leakage term

In this paper, we propose a class of quaternion-valued shunting inhibitory cellular neural networks of neutral type with time delays in the leakage term. In order to overcome the difficulty of the non-commutativity of quaternion multiplication, we first decompose the system under consideration into four real-valued systems. Then by using the exponential dichotomy theory of linear differential equations and Banach's fixed point theorem, we establish sufficient conditions to ensure the existence and global exponential stability of almost periodic solutions for this class of neural networks. Finally, a numerical example is given to demonstrate the effectiveness of the obtained result.     

Existence and global exponential stability of periodic solutions of recurrent cellular neural networks with impulses and delays

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness and global exponential stability of periodic solutions for recurrent neural networks with impulsive perturbations and delays. Further, by using numerical simulation method, the influences of the impulsive perturbations on the inherent oscillations are investigated.     

Almost periodic solutions for Hopfield neural networks with continuously distributed delays

In this paper Hopfield neural networks with continuously distributed delays are considered. Without assuming the global Lipschitz conditions of activation functions, sufficient conditions for the existence and exponential stability of the almost periodic solutions are established by using the fixed point theorem and differential inequality techniques. The results of this paper are new and they complement previously known results.     

Periodic Solution for Fuzzy Cohen–Grossberg BAM Neural Networks with Both Time-Varying and Distributed Delays and Variable Coefficients

In this paper, we investigate the existence and global exponential stability of periodic solution for a general class of fuzzy Cohen–Grossberg bidirectional associative memory (BAM) neural networks with both time-varying and (finite or infinite) distributed delays and variable coefficients. Some novel sufficient conditions for ascertaining the existence, uniqueness, global attractivity and exponential stability of the periodic solution to the considered system are obtained by applying matrix theory, inequality analysis technique and contraction mapping principle. The results remove the usual assumption that the activation functions are bounded and/or continuously differentiable. It is believed that these results are significant and useful for the design and applications of fuzzy Cohen–Grossberg BAM neural networks. Moreover, an example is employed to illustrate the effectiveness and feasibility of the results obtained here.     

Pseudo Almost Periodic Solutions for High-Order Hopfield Neural Networks with Time-Varying Leakage Delays

In this paper, high-order Hopfield neural networks with time-varying leakage delays are investigated. By applying Lyapunov functional method and differential inequality techniques, a set of sufficient conditions are obtained for the existence and exponential stability of pseudo almost periodic solutions of the model. Some simulations are carried out to support the theoretical findings. Our results improve and generalize those of the previous studies.     

Global Exponential Stability and Existence of Periodic Solution of Impulsive Cohen–Grossberg Neural Networks with Distributed Delays on Time Scales

On time scales, by using the continuation theorem of coincidence degree theory, M-matrix theory and constructing some suitable Lyapunov functions, some sufficient conditions are obtained for the existence and exponential stability of periodic solutions of impulsive Cohen–Grossberg neural networks with distributed delays, which are new and complement of previously known results. Finally, an example is given to illustrate the effectiveness of our main results.