Matrix measure strategies for stability and synchronization of inertial BAM neural network with time delays

被引：325

作者：

Cao, Jinde ^{[1
,2
,3
]}

Wan, Ying ^{[1
,2
]}

机构：

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

[2] Southeast Univ, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China

[3] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia

来源：

NEURAL NETWORKS | 2014年 / 53卷

基金：

中国国家自然科学基金;

关键词：

Global exponential stability; Global synchronization; Matrix measure; Inertial BAM neural network; Time-varying delays; PERIODIC-SOLUTIONS; BIFURCATION-ANALYSIS; PATTERN-FORMATION; MODEL; SYSTEM;

D O I：

10.1016/j.neunet.2014.02.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A single inertial BAM neural network with time-varying delays and external inputs is concerned in this paper. First, by choosing suitable variable substitution, the original system can be transformed into first-order differential equations. Then, we present several sufficient conditions for the global exponential stability of the equilibrium by using matrix measure and Halanay inequality, these criteria are simple in form and easy to verify in practice. Furthermore, when employing an error-feedback control term to the response neural network, parallel criteria regarding to the exponential synchronization of the drive-response neural network are also generated. Finally, some examples are given to illustrate our theoretical results. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：165 / 172

页数：8

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