Exponential stability of Cohen-Grossberg-type BAM neural networks with time-varying delays via impulsive control

被引：80

作者：

Li, Xiaodi ^{[1
]}

机构：

[1] Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China

来源：

NEUROCOMPUTING | 2009年 / 73卷 / 1-3期

关键词：

Cohen-Grossberg-type BAM neural networks; Exponential stability; Time-varying delays; Impulsive control; PERIODIC-SOLUTION; ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; GLOBAL STABILITY; EXISTENCE; COEFFICIENTS; CRITERION;

D O I：

10.1016/j.neucom.2009.04.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a class of Cohen-Grossberg-type BAM neural networks with time-varying delays are studied. Some sufficient conditions are established for the existence, uniqueness and exponential stability of the equilibrium point by using Lyapunov functionals, the analysis method and impulsive control. Here we point out that our result, which is different from previous known results, shows that the unstable Cohen-Grossberg-type BAM neural networks with time-varying delays can be exponentially stabilized via impulsive control. Moveover, the estimate of the exponential convergence rate is also obtained, which depends on the system parameters. Finally, an illustrative example is given to show the effectiveness of the proposed method and result. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：525 / 530

页数：6

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