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Exponential stability of impulsive high-order Hopfield-type neural networks with time-varying delays

被引：115
|
作者：
Liu, XZ [1 ]
Teo, KL
Xu, BJ
机构：
[1] Univ Waterloo, Dept Appl Math, Waterloo, ON N2L 3G1, Canada
[2] Curtin Univ Technol, Dept Math & Stat, Perth, WA 6845, Australia
[3] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Hubei, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 06期
基金：
中国国家自然科学基金; 加拿大自然科学与工程研究理事会; 中国博士后科学基金;
关键词：
exponential stability; impulsive high-order Hopfield-type Lyapunov function; neural networks;
D O I：
10.1109/TNN.2005.857949
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper considers the problems of global exponential stability and exponential convergence rate for impulsive high-order Hopfield-type neural networks with time-varying delays. By using the method of Lyapunov functions, some sufficient conditions for ensuring global exponential stability of these networks are derived, and the estimated exponential convergence rate is also obtained. As an illustration, an numerical example is worked out using the results obtained.
引用
收藏
页码：1329 / 1339
页数：11
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