A new stability criterion for bidirectional associative memory neural networks of neutral-type

被引：172

作者：

Park, Ju H. ^{[1
]}

Park, C. H. ^{[2
]}

Kwon, O. M. ^{[3
]}

Lee, S. M. ^{[4
]}

机构：

[1] Yeungnam Univ, Dept Elect Engn, Robust Control & Nonlinear Dynam Lab, Kyongsan 712749, South Korea

[2] Yeungnam Univ, Dept Comp Engn, Kyongsan 712749, South Korea

[3] Chungbuk Natl Univ, Sch Elect & Comp Engn, Cheongju, South Korea

[4] KT Co Ltd, BcN Business Unit, Platform Verificat Div, Taejon, South Korea

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 199卷 / 02期

关键词：

global stability; BAM neural network; delay; linear matrix inequality; Lyapunov method;

D O I：

10.1016/j.amc.2007.10.032

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In the paper, the global asymptotic stability of equilibrium is considered for continuous bidirectional associative memory ( BAM) neural networks of neutral type by using the Lyapunov method. A new stability criterion is derived in terms of linear matrix inequality ( LMI) to ascertain the global asymptotic stability of the BAM. The LMI can be solved easily by various convex optimization algorithms. A numerical example is illustrated to verify our result. (C) 2007 Elsevier Inc. All rights reserved.

引用

页码：716 / 722

页数：7

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