Improved exponential stability criteria for neural networks with time-varying delays

被引：28
|
作者：
Tian, Junkang [1 ]
Zhong, Shouming [1 ,2 ]
Wang, Yong [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Minist Educ, Key Lab Neuroinformat, Chengdu 611731, Sichuan, Peoples R China
[3] SW Petr Univ, State Key Lab Oil & Gas Reservoir Geol & Exploita, Chengdu 610500, Sichuan, Peoples R China
来源：
NEUROCOMPUTING | 2012年 / 97卷
关键词：
Exponential stability; Neural networks; Time-varying delays; Linear matrix inequality (LMI); GLOBAL ASYMPTOTIC STABILITY; GENERAL ACTIVATION FUNCTIONS; DEPENDENT STABILITY; DISTRIBUTED DELAYS; LMI APPROACH; DISCRETE; INTERVAL;
D O I：
10.1016/j.neucom.2012.05.018
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 concerned the problem of exponential stability criteria for neural networks with discrete and distributed time-varying delays. By dividing the delay interval into multiple segments and choosing a new Lyapunov functional, some improved stability criteria are derived in terms of linear matrix inequalities. The obtained criteria are less conservative due to a convex optimization approach is considered. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：164 / 173
页数：10
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