New criteria on delay-dependent stability for discrete-time neural networks with time-varying delays

被引：70
|
作者：
Kwon, O. M. [1 ]
Park, M. J. [1 ]
Park, Ju H. [2 ]
Lee, S. M. [3 ]
Cha, E. J. [4 ]
机构：
[1] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 361763, South Korea
[2] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea
[3] Daegu Univ, Sch Elect Engn, Gyongsan 712714, South Korea
[4] Chungbuk Natl Univ, Dept Biomed Engn, Sch Med, Cheongju 361763, South Korea
来源：
NEUROCOMPUTING | 2013年 / 121卷
基金：
新加坡国家研究基金会;
关键词：
Discrete-time neural networks; Time-varying delay; Stability; Lyapunov method; EXPONENTIAL STABILITY; ROBUST STABILITY; SYSTEMS; STABILIZATION; ARRAYS;
D O I：
10.1016/j.neucom.2013.04.026
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the problem of delay-dependent stability for discrete-time neural networks with time-varying delays is investigated. By constructing a newly augmented Lyapunov-Krasovskii functional, a sufficient condition for guaranteeing the asymptotic stability of the concerned network is derived in the framework of linear matrix inequalities. Also, a further improved stability condition is developed by proposing a new activation condition which has not been considered in the literature. Two numerical examples are given to illustrate the effectiveness of the proposed methods. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：185 / 194
页数：10
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