Global robust asymptotic stability analysis of BAM neural networks with time delay and impulse: An LMI approach

被引：21

作者：

Zhou, Qinghua ^{[1
]}

Wan, Li ^{[2
]}

机构：

[1] Zhaoqing Univ, Dept Math, Zhaoqing 526061, Peoples R China

[2] Wuhan Univ Sci & Engn, Dept Math & Phys, Wuhan 430073, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2010年 / 216卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Bi-directional associative memory neural networks; Impulse; Robust asymptotic stability; Linear matrix inequality; Lyapunov-Krasovskii functional; EXPONENTIAL STABILITY; DISTRIBUTED DELAYS; PERIODIC-SOLUTION; EXISTENCE;

D O I：

10.1016/j.amc.2010.03.003

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays and impulse is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results. (C) 2010 Elsevier Inc. All rights reserved.

引用

页码：1538 / 1545

页数：8

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