An exponential convergence estimate for analog neural networks with delay

被引：45

作者：

Chu, TG ^{[1
]}

机构：

[1] Peking Univ, Dept Mech & Engn Sci, Ctr Syst & Control, Beijing 100871, Peoples R China

来源：

PHYSICS LETTERS A | 2001年 / 283卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

neural networks; time delay; global exponential convergence; comparison principle;

D O I：

10.1016/S0375-9601(01)00171-2

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

A componentwise estimate of exponential convergence is obtained for a class of delayed Hopfield type neural networks by using a method based on a comparison principle of delay differential systems. The method is simple and straightforward in analysis, without resorting to any Lyapunov functionals. The result shows explicitly the effect of time delay on exponential decay rate of the networks and is of practical significance for designing fast and stable neural networks. Some existing results via Lyapunov functional method and linear analysis are found to be special cases of the present result. (C) 2001 Elsevier Science B.V. All rights reserved.

引用

页码：113 / 118

页数：6

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