Exponential stability criteria for discrete-time recurrent neural networks with time-varying delay

被引：34
|
作者：
Yu, Jianjiang [1 ,2 ]
Zhang, Kanjian [1 ]
Fei, Shumin [1 ]
机构：
[1] Minist Educ Res, 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：
NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS | 2010年 / 11卷 / 01期
关键词：
Delay-dependent; Discrete-time; RNNs; Exponential stability; Linear matrix inequality (LMI); GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY;
D O I：
10.1016/j.nonrwa.2008.10.053
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, the robust global exponential stability is investigated for the discrete-time recurrent neural networks (RNNs) with time-varying interval delay. By choosing an augmented Lyapunov-Krasovskii functional, delay-dependent results guaranteeing the global exponential stability and the robust exponential stability of the concerned neural network are obtained. The results are shown to be a generalization of some previous results, and less conservative than the existing works. Two numerical examples are given to demonstrate the applicability of the proposed method. (C) 2008 Elsevier Ltd. All rights reserved.
引用
收藏
页码：207 / 216
页数：10
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