On global exponential stability of high-order neural networks with time-varying delays

被引：33

作者：

Zhang, Baoyong

Xu, Shengyuan ^{[1
]}

Li, Yongmin

Chu, Yuming

机构：

[1] Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China

[2] Huzhou Teachers Coll, Dept Math, Huzhou 313000, Zhejiang, Peoples R China

来源：

PHYSICS LETTERS A | 2007年 / 366卷 / 1-2期

基金：

中国国家自然科学基金;

关键词：

global exponential stability; high-order neural networks; linear matrix inequality (LMI); time-varying delays;

D O I：

10.1016/j.physleta.2007.01.065

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This Letter investigates the problem of stability analysis for a class of high-order neural networks with time-varying delays. The delays are bounded but not necessarily differentiable. Based on the Lyapunov stability theory together with the linear matrix inequality (LMI) approach and the use of Halanay inequality, sufficient conditions guaranteeing the global exponential stability of the equilibrium point of the considered neural networks are presented. Two numerical examples are provided to demonstrate the effectiveness of the proposed stability criteria. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：69 / 78

页数：10

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