Simplified exponential stability analysis for recurrent neural networks with discrete and distributed time-varying delays

被引：23
|
作者：
Yu, Jianjiang [1 ,2 ]
Zhang, Kanjian [1 ]
Fei, Shumin [1 ]
Li, Tao [1 ]
机构：
[1] Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Peoples R China
[2] Yancheng Teachers Univ, Sch Informat Sci & Technol, Yancheng 224002, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2008年 / 205卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Neural networks (NNs); Exponential stability; Delay-dependent; Linear matrix inequality (LMI);
D O I：
10.1016/j.amc.2008.08.022
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper provides simplified exponential stability criteria for a class of recurrent neural networks (RNNs) with discrete and distributed time-varying delays. The activation functions of the RNNs are assumed to be more general, and the proposed criteria are obtained by only using a integral inequality and are not involved any free-weighting matrices. This feature makes the computational burden largely reduced. Numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码：465 / 474
页数：10
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