Improved global exponential stability criteria of cellular neural networks with time-varying delays

In this paper, the existence and uniqueness of the equilibrium point and stability of the cellular neural networks (CNNs) with time-varying delays are analyzed and proved. Several global exponential stability conditions of the neural networks are obtained by the delay differential inequality and matrix measures approach. The obtained results are extensions of the earlier literature. The approach used in this paper is also suitable for delayed Hopfield neural networks and delayed bi-directional associative memory neural networks whose activation functions are often nondifferentiable or unbounded. Two simulation examples in comparison to previous results in literature are shown to check the theory in this paper.     

Existence and global exponential stability of almost periodic solution for CNNs with variable coefficients and delays

By using exponential dichotomy and the Banach fixed-point theory and combining Yang inequality, some sufficient conditions are derived ensuring existence and global exponential stability of almost periodic solution for CNNs with variable coefficients and delays. Without assuming the boundedness of signal function, these results obtained here can be expected to have highly important significance in designs and applications of the networks. We extend and improve previously known results.     

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.     

Decentralized feedback control of uncertain cellular neural networks subject to time-varying delays and dead-zone input

This paper investigates the stabilization problem for uncertain cellular neural networks (CNNs) subject to time-varying delays and dead-zone input. On the basis of Lyapunov stability theory, a memoryless decentralized feedback control law is derived for guaranteeing global exponential stability of the system. The main results illustrate that the derived control law does not impose restriction on the derivative of the time-varying delays and can be applied to stabilizing the uncertain CNNs with time-varying delays and dead-zone input. An illustrative example is given to justify the validity and feasibility of the proposed control scheme.     

Exponential stability criteria for discrete-time recurrent neural networks with time-varying delay

In this paper, the robust global exponential stability is investigated for the discrete-time recurrent neural networks (RNNs) with time-varying interval delay. By choosing an augmented Lyapunov–Krasovskii functional, delay-dependent results guaranteeing the global exponential stability and the robust exponential stability of the concerned neural network are obtained. The results are shown to be a generalization of some previous results, and less conservative than the existing works. Two numerical examples are given to demonstrate the applicability of the proposed method.     

Stability analysis of stochastic functional differential equations with infinite delay and its application to recurrent neural networks

In this paper, we investigate the stochastic functional differential equations with infinite delay. Some sufficient conditions are derived to ensure the pth moment exponential stability and pth moment global asymptotic stability of stochastic functional differential equations with infinite delay by using Razumikhin method and Lyapunov functions. Based on the obtained results, we further study the pth moment exponential stability of stochastic recurrent neural networks with unbounded distributed delays. The result extends and improves the earlier publications. Two examples are given to illustrate the applicability of the obtained results.     

LMI-based criteria for stability of high-order neural networks with time-varying delay

This paper investigates the stability of a class of high-order neural networks with time-varying delay, which can be considered as an expansion of Hopfield neural networks and is seldom considered in the literature. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, sufficient conditions guaranteeing the global exponential stability of the equilibrium point are presented. Two examples are given to show the effectiveness of the proposed conditions. The obtained results are also shown to be different from and more general than existing ones.     

具双时滞的SEIRS传染病模型指数收敛性

本文研究了一类具指数人口统计与结构的SEIRS传染病模型,利用一种新的基于比较原理的分析,获得了无病平衡点局部和全局指数稳定的两个充分条件.     

含混合时滞和脉冲的Cohen-Grossberg神经网络的全局指数稳定性

本文讨论了含混合时滞和脉冲的Cohen-Grossberg神经网络的稳定性.通过应用M矩阵理论和不等式技巧,得到了含混合时滞的Cohen-Grossberg神经网络平衡态的全局指数稳定性的充分条件.相比以前同类文献,本文减弱了部分条件,推广了部分结论,并在文末给出了两个示例.本文结论对于设计和应用神经网络有一定实用价值.     

Exponential stability for nonautonomous neural networks with variable delays

In this paper, by utilizing a delay differential inequality and combining with inequality analysis technique, we investigate global exponential stability for nonautonomous neural networks with variable delays. Some new sufficient conditions ensuring global exponential stability are obtained. An example is also given to demonstrate the effectiveness of the obtained results.     

Global exponential stability for shunting inhibitory CNNs with delays

In this paper, the global exponential stability for shunting inhibitory cellular neural networks (SICNNs) with delays is studied by constructing suitable Lyapunov functionals and applying some critical analysis techniques. The sufficient conditions guaranteeing the network’s global exponential stability are obtained. Our results impose less restrictive conditions than those in the references. Moreover an example is given to illustrate the feasibility of the conditions in our results.     

Global exponential stability of impulsive discrete-time neural networks with time-varying delays

This paper studies the problem of global exponential stability and exponential convergence rate for a class of impulsive discrete-time neural networks with time-varying delays. Firstly, by means of the Lyapunov stability theory, some inequality analysis techniques and a discrete-time Halanay-type inequality technique, sufficient conditions for ensuring global exponential stability of discrete-time neural networks are derived, and the estimated exponential convergence rate is provided as well. The obtained results are then applied to derive global exponential stability criteria and exponential convergence rate of impulsive discrete-time neural networks with time-varying delays. Finally, numerical examples are provided to illustrate the effectiveness and usefulness of the obtained criteria.     

变时滞细胞神经网络稳定性分析

利用矩阵测度、Liapunov函数和Halanay时滞不等式的方法研究了具有变时滞的细胞神经网络模型平衡点的全局指数稳定性问题.给出了判定平衡点全局指数稳定性的几个代数判据,可用于时滞细胞神经网络的设计与检验,数值算例说明其结果的优越性.     

NEW CRITERIA OF EXPONENTIAL STABILITY FOR BAM NEURAL NETWORKS WITH DELAYS

In this Letter,a novel Lyapunov functional is constructed to investigate the exponential stability of the BAM neural networks.New sufficient conditions of the uniqueness and global exponential stability for the equilibrium of BAM neural networks with delays are obtained.The results improve those existing ones.     

On contraction of nonlinear difference equations with time‐varying delays

General nonlinear difference equations with time‐varying delays are considered. Explicit criteria for contraction of such equations are presented. Then some simple sufficient conditions for global exponential stability of equilibria and for stability of invariant sets are derived. Furthermore, explicit criteria for existence, uniqueness and global exponential stability of periodic solutions are derived. Finally, the obtained results are applied to time‐varying discrete‐time neural networks with delay.     

Stability analysis of Cohen–Grossberg neural network with both time-varying and continuously distributed delays

In this paper, the Cohen–Grossberg neural network model with both time-varying and continuously distributed delays is considered. Without assuming both global Lipschitz conditions on these activation functions and the differentiability on these time-varying delays, applying the idea of vector Lyapunov function, M-matrix theory and inequality technique, several new sufficient conditions are obtained to ensure the existence, uniqueness, and global exponential stability of equilibrium point for Cohen–Grossberg neural network with both time-varying and continuously distributed delays. These results generalize and improve the earlier publications. Two numerical examples are given to show the effectiveness of the obtained results. It is believed that these results are significant and useful for the design and applications of the Cohen–Grossberg neural networks.     

Global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays

In this paper, by means of constructing the extended impulsive delayed Halanay inequality and by Lyapunov functional methods, we analyze the global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays. Some new sufficient conditions ensuring exponential stability of the unique equilibrium point of impulsive Hopfield neural networks with time delays are obtained. Those conditions are more feasible than that given in the earlier references to some extent. Some numerical examples are also discussed in this work to illustrate the advantage of the results we obtained.     

Global exponential stability analysis of Cohen–Grossberg neural network with delays

This brief presents new sufficient conditions of globally exponential stability of Cohen–Grossberg neural networks with time delays. The results are also compared with previously reported results in the literature, implying that the results obtained in this paper provide one more set of criteria for determining the global exponential stability of Cohen–Grossberg neural networks with time delays.     

具S-型分布时滞的模糊细胞神经网络的周期解及指数稳定性

研究了一类具S-型分布时滞的模糊细胞神经网络(FCNN)的周期解及全局指数稳定性问题.在不要求激励函数全局L ipsch itz条件下,通过使用指数型二分性和Schauder不动点定理以及构造Lyapunov函数,得到了模糊细胞神经网络模型周期解和指数稳定性的一些充分条件.此外,给出一个实例说明结果是可行的.     

一类变时滞神经网络的全局指数稳定性

本文研究一类变时滞神经网络平衡点的全局指数稳定性.在不要求激活函数全局Lipschitz条件下,利用Lyapunov函数方法,并结合Young不等式和Halanay时滞微分不等式,得到了系统全局指数稳定的充分条件.文末,一个数值例子用以说明本文结果的有效性.