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

被引：44
|
作者：
Xu, Honglei [1 ,2 ]
Chen, Yuanqiang [3 ]
Teo, Kok Lay [1 ]
机构：
[1] Curtin Univ Technol, Dept Math & Stat, Perth, WA 6845, Australia
[2] Guizhou Univ, Dept Math, Guiyang 550025, Peoples R China
[3] Natl Minor Coll Guizhou, Dept Math, Guiyang 550025, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2010年 / 217卷 / 02期
基金：
澳大利亚研究理事会; 中国国家自然科学基金;
关键词：
Impulsive discrete-time neural networks; Global exponential stability; Exponential convergence rate; Halanay inequality; DISTRIBUTED DELAYS; DIFFERENTIAL-EQUATIONS; SYNCHRONIZATION; STABILIZATION; SYSTEMS; MODELS;
D O I：
10.1016/j.amc.2010.05.087
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码：537 / 544
页数：8
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