Global stability analysis for delayed complex-valued BAM neural networks

被引：71

作者：

Wang, Zengyun ^{[1
]}

Huang, Lihong ^{[2
,3
]}

机构：

[1] Hunan First Normal Univ, Dept Math, Changsha 410205, Hunan, Peoples R China

[2] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China

[3] Hunan Womens Univ, Changsha 410003, Hunan, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 173卷

基金：

中国国家自然科学基金;

关键词：

Complex-valued BAM neural networks; Global stability; Delay-dependent stability criteria; Linear matrix inequality; Time delay; EXPONENTIAL STABILITY; SYSTEM;

D O I：

10.1016/j.neucom.2015.09.086

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 investigates the stability criteria for complex-valued bidirectional associative memory (BAM) with time delay. Firstly, based on the Lyapunov function method and mathematical analysis technique, sufficient conditions for the existence of unique equilibrium, and global asymptotic stability of the equilibrium are derived by separating complex-valued neural networks into real and imaginary parts. Secondly, we propose the delay-dependent stability criteria for the complex-valued BAM neural networks. Finally, numerical examples are given to show the effectiveness and merits of the present results. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：2083 / 2089

页数：7

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