Stability and passivity analysis for uncertain discrete-time neural networks with time-varying delay

被引：34
|
作者：
Shu, Yanjun [1 ]
Liu, Xinge [1 ]
Liu, Yajuan [2 ]
机构：
[1] Cent S Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
[2] Acad Sinica, Acad Math & Syst Sci, Beijing 100190, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 173卷
关键词：
Discrete-time neural networks; Time-varying delay; Stability; Passivity; Summation inequality; GLOBAL ASYMPTOTIC STABILITY; CRITERIA;
D O I：
10.1016/j.neucom.2015.09.043
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 stability and passivity analysis for uncertain discrete-time neural networks with time-varying delay is investigated. By constructing a new Lyapunov-Krasovskii functional and employing a novel summation inequality which is a discrete-time counterpart of the Wirtinger-based integral inequality, a less conservative robust stability criterion is derived in terms of linear matrix inequalities. Furthermore, a new sufficient condition is established to assure the considered neural networks to be passive. Several numerical examples are provided to demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：1706 / 1714
页数：9
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