New results on robust exponential stability for discrete recurrent neural networks with time-varying delays

被引：30
|
作者：
Wu, Zhengguang [1 ]
Su, Hongye [1 ]
Chu, Jian [1 ]
Zhou, Wuneng [2 ]
机构：
[1] Zhejiang Univ, Inst Cyber Syst & Control, Natl Lab Ind Control Technol, Hangzhou 310027, Peoples R China
[2] Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China
来源：
NEUROCOMPUTING | 2009年 / 72卷 / 13-15期
基金：
国家高技术研究发展计划(863计划);
关键词：
Neural networks; Time-varying delays; Delay-dependent; Exponential stability; Linear matrix inequality (LMI); DEPENDENT ASYMPTOTIC STABILITY; DISTRIBUTED DELAYS; STATE ESTIMATION; VARIABLE DELAYS; LMI APPROACH; CRITERIA; SYSTEMS; PERIODICITY;
D O I：
10.1016/j.neucom.2009.01.010
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 is concerned with the problem of robust exponential stability analysis for uncertain discrete recurrent neural networks with time-varying delays. in terms of linear matrix inequality (LMI) approach, some novel stability conditions are proposed via a new Lyapunov function. Neither any model transformation nor free-weighting matrices are employed in our theoretical derivation. The established stability criteria significantly improve and simplify some existing stability conditions. Numerical examples are given to demonstrate the effectiveness of the proposed methods. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：3337 / 3342
页数：6
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