Delay-dependent exponential stability of cellular neural networks with time-varying delays

The global exponential stability of cellular neural networks (CNNs) with time-varying delays is analyzed. Two new sufficient conditions ensuring global exponential stability for delayed CNNs are obtained. The conditions presented here are related to the size of delay. The stability results improve the earlier publications. Two examples are given to demonstrate the effectiveness of the obtained results.     

Global exponential stability of a class of neural networks with delays

This paper is concerned with the stability of neural networks with time-varying delays. Under assumption that the nonlinear stimulate functions are Lipschitz continuous, by means of generalized Halanay inequalities, Dini＇s derivative and functional analysis techniques, several globally exponential stability criteria are established, which are only dependent on the parameters of the system.     

Delay-dependent asymptotic stability for neural networks with distributed delays

In this paper, dynamical behavior of a class of neural networks with distributed delays is studied by employing suitable Lyapunov functionals, delay-dependent criteria to ensure local and global asymptotic stability of the equilibrium of the neural networks. Our results are applied to classical Hopfield neural networks with distributed delays and some novel asymptotic stability criteria are also derived. The obtained conditions are shown to be less conservative and restrictive than those reported in the known literature.     

Global exponential stability of a class of neural networks with unbounded delays

In this paper, the global exponential stability of a class of neural networks is investigated. The neural networks contain variable and unbounded delays. By constructing a suitable Lyapunov function and using the technique of matrix analysis, we obtain some new sufficient conditions for global exponential stability. Published in Ukrains’kyi Matematychnyi Zhurnal, Vol. 60, No. 10, pp. 1401–1413, October, 2008.     

Lagrange exponential stability of quaternion-valued memristive neural networks with time delays

This paper investigates the Lagrange global exponential stability of the quaternion-valued memristive neural networks (QVMNNs). Two kinds of activation functions based on different assumptions are considered. Then, based on the Lyapunov function approach, decomposition method, and some inequality skills, two novel sufficient conditions for lagrange stability of QVMNNs are provided corresponding to different types of activation functions. Lastly, simulation examples are provided to demonstrate the correctness of our theoretical results.     

Global exponential stability in Lagrange sense for recurrent neural networks with time delays

In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks.     

时滞Hopfield神经网络的全局指数稳定性

讨论带有可变时滞的Hopfield神经网络的全局指数稳定性.在非线性激励函数满足Lipschitz条件的假设下,利用推广的Halanay不等式,Dini导数和分析技巧,建立了这类神经网络系统全局指数稳定的几个判别准则.这些判别准则仅仅依赖于系统的参数.     

Global exponential stability of reaction-diffusion cellular neural networks with S-type distributed time delays

The authors discuss the existence of the equilibrium point and its global exponential stability of reaction-diffusion cellular neural networks (RDCNNs) with S-type distributed signal transmission delays along the axon of a neuron by means of the topological degree theory and differential inequality technique. A theorem and two corollaries were obtained in which the boundedness, monotonicity and differentiability conditions on the activation functions are not required. The sufficient conditions on global exponential stability established in this paper, which are easily verifiable, have a wider adaptive range.     

Global exponential stability for a class of neural networks

In this paper, the global exponential stability for a class of neural networks is investigated. A simple criterion ensuring global exponential stability is established, which is a less restrictive version of a recent criterion due to Park and Kwon. Some examples showing the effectiveness of the present criterion are given.     

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 for nonautonomous cellular neural networks with unbounded delays

Global exponential stability of nonautonomous cellular neural networks with unbounded delays is considered in this paper. By applying Lyapunov functional method, some new sufficient conditions are given for global exponential stability of solutions of the networks. The stability conditions obtained here improve and extend some of the previous conditions. An example is presented to illustrate the applicability of these conditions.     

Global exponential stability of Cohen-Grossberg neural networks with variable delays

A class of generalized Cohen-Grossberg neural networks（CGNNs） with variable de- lays are investigated. By introducing a new type of Lyapunov functional and applying the homeomorphism theory and inequality technique, some new conditions axe derived ensuring the existence and uniqueness of the equilibrium point and its global exponential stability for CGNNs. These results obtained are independent of delays, develop the existent outcome in the earlier literature and are very easily checked in practice.     

Global exponential stability of interval neural networks with variable delays

In this work, the conditions ensuring existence, uniqueness, and global exponential stability of the equilibrium point of interval neural networks with variable delays are studied. Applying the idea of the vector Lyapunov function, and M-matrix theory, the sufficient conditions for global exponential stability of interval neural networks are obtained.     

Global exponential stability of Hopfield neural networks with distributed delays

In this paper, dynamical behaviors of Hopfield neural networks system with distributed delays were studied. By using contraction mapping principle and differential inequality technique, a sufficient condition was obtained to ensure the existence uniqueness and global exponential stability of the equilibrium point for the model. Here we point out that our methods, which are different from previous known results, base on the contraction mapping principle and inequality technique. Two remarks were also worked out to demonstrate the advantage of our results.     

Robust stability for stochastic Hopfield neural networks with time delays

In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic neural networks with time delays and parameter uncertainties. The delays are time-invariant, and the uncertainties are norm-bounded that enter into all the network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov–Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be checked readily by using some standard numerical packages, and no tuning of parameters is required. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.     

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.     

Global exponential stability of fuzzy BAM neural networks with transmission delays

In this paper, a class of fuzzy bidirectional associated memory (BAM) neural networks with transmission delays are studied. Some sufficient conditions are established for the existence , uniqueness and global exponential stability of equilibrium point. The sufficient conditions are easy to verify at pattern recognition and automatic control. Finally, an example is given to show feasibility and effectiveness of our results.     

Global exponential stability for a class of generalized delayed neural networks with impulses

In this paper, by utilizing Lyapunov functional method, the quality of negative definite matrix and the linear matrix inequality approach, the global exponential stability of the equilibrium point for a class of generalized delayed neural networks with impulses is investigated. A new criterion on global exponential stability is obtained. The result is related to the size of delays and impulses. An example is given to illustrate the effectiveness of our result. Copyright © 2011 John Wiley & Sons, Ltd.     

New LMI conditions for global exponential stability of cellular neural networks with delays

In this paper, we investigate further cellular neural networks model with delays. We allow the model to have nondifferentiable and unbounded signal function, and use linear matrix inequality (LMI) framework and Homeomorphism theorem to prove the existence and uniqueness of the equilibrium, and derive some new sufficient condition ensuring global exponential stability of the networks. Two examples with their numerical simulations are provided to show the correctness of our analysis. It is believed that these results are significant and useful for the design and applications of cellular neural networks.     

pth moment exponential synchronization analysis for a class of stochastic neural networks with mixed delays

In this paper, the issue of pth moment exponential synchronization of a class of stochastic neural networks with mixed delays is investigated. By establishing two new integro-differential inequalities, some new sufficient conditions ensuring pth moment exponential synchronization are obtained. Compared with previous method, our method does not resort to any Lyapunov function. The results obtained in this paper generalize some earlier works reported in the literature. Some strict constraints of time delays and kernel functions are removed. Two numerical examples are presented to illustrate the validity of the main results.