Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays

被引：41
|
作者：
Deng, Feiqi [2 ]
Hua, Mingang [1 ,2 ]
Liu, Xinzhi [1 ]
Peng, Yunjian [2 ]
Fei, Juntao [3 ]
机构：
[1] Univ Waterloo, Dept Appl Math, Waterloo, ON N2L 3G1, Canada
[2] S China Univ Technol, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China
[3] Hohai Univ, Coll Comp & Informat, Changzhou 213022, Peoples R China
来源：
NEUROCOMPUTING | 2011年 / 74卷 / 10期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Stochastic neural networks; Exponential stability; Linear matrix inequality (LMI); DISCRETE; CRITERIA;
D O I：
10.1016/j.neucom.2010.08.027
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 robust delay-dependent exponential stability of uncertain stochastic neural networks (SNNs) with mixed delays. Based on a novel Lyapunov-Krasovskii functional method, some new delay-dependent stability conditions are presented in terms of linear matrix inequalities, which guarantee the uncertain stochastic neural networks with mixed delays to be robustly exponentially stable. Numerical examples are given to illustrate the effectiveness of our results. (C) 2011 Elsevier B.V. All rights reserved.
引用
收藏
页码：1503 / 1509
页数：7
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