Improved exponential stability criteria for discrete-time neural networks with time-varying delay

被引：13
|
作者：
Liu, Zixin [1 ,2 ]
Lue, Shu [1 ]
Zhong, Shouming [1 ]
Ye, Mao [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Appl Math, Chengdu 610054, Peoples R China
[2] Guizhou Coll Finance & Econ, Sch Math & Stat, Guiyang 550004, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 610054, Peoples R China
来源：
NEUROCOMPUTING | 2010年 / 73卷 / 4-6期
基金：
中国国家自然科学基金;
关键词：
Delay-dependent stability; Discrete-time neural networks; Global exponential stability; Time-varying delays; Robust exponential stability; GLOBAL ASYMPTOTIC STABILITY; ROBUST STABILITY; SYSTEMS;
D O I：
10.1016/j.neucom.2009.08.017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The problem of robust exponential stability for a class of discrete-time recurrent neural networks with time-varying delay is investigated. By constructing a new augmented Lyapunov-Krasovskii functional, some new delay-dependent stable criteria are obtained. These criteria are formulated in the forms of linear matrix inequality (LMI). Compared with some previous results, the new conditions obtained in this paper are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码：975 / 985
页数：11
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