Exponential stability of high-order bidirectional associative memory neural networks with time delays

被引：183

作者：

Cao, JD ^{[1
]}

Liang, JL

Lam, J

机构：

[1] Southeast Univ, Dept Math, Nanjing 210096, Peoples R China

[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China

来源：

PHYSICA D-NONLINEAR PHENOMENA | 2004年 / 199卷 / 3-4期

基金：

中国国家自然科学基金;

关键词：

high-order neural networks; exponential stability; bidirectional associative memory (BAM); time delays; linear matrix inequality; Lyapunov functional;

D O I：

10.1016/j.physd.2004.09.012

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, exponential stability is studied for a class of high-order bidirectional associative memory (BAM) neural networks with time delays. By employing the linear matrix inequality (LMI) and the Lyapunov functional methods, several sufficient conditions are obtained for ensuring the system to be globally exponentially stable. Two illustrative examples are also given in the end of this paper to show the effectiveness of our results. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：425 / 436

页数：12

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