A new approach to exponential stability analysis of neural networks with time-varying delays

被引：154

作者：

Xu, SY ^{[1
]}

Lam, J

机构：

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

[2] Univ Hong Kong, Dept Mech Engn, Hong Kong, Hong Kong, Peoples R China

来源：

NEURAL NETWORKS | 2006年 / 19卷 / 01期

关键词：

global exponential stability; linear matrix inequality; neural networks; time-varying delay systems;

D O I：

10.1016/j.neunet.2005.05.005

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 considers the problem of exponential stability analysis of neural networks with time-varying delays. The activation functions are assumed to be globally Lipschitz continuous. A linear matrix inequality (LMI) approach is developed to derive sufficient conditions ensuring the delayed neural network to have a unique equilibrium point, which is globally exponentially stable. The proposed LMI conditions can be checked easily by recently developed algorithms solving LMIs. Examples are provided to demonstrate the reduced conservativeness of the proposed results. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：76 / 83

页数：8

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