Exponential stability criterion for interval neural networks with discrete and distributed delays

被引：25

作者：

Chen, Hao ^{[1
,2
]}

Zhong, Shouming ^{[1
]}

Shao, Jinliang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China

[2] Huaibei Normal Univ, Sch Math Sci, Huaibei 235000, Anhui, Peoples R China

来源：

APPLIED MATHEMATICS AND COMPUTATION | 2015年 / 250卷

关键词：

Neural networks; Lyapunov functional; Distributed delay; Exponential stability; M-matrix theory; TIME-VARYING DELAYS; GLOBAL ROBUST STABILITY; SLIDING MODE CONTROLLER; MARKOV JUMP SYSTEMS; DEPENDENT STABILITY; INFINITY CONTROL; BOUNDEDNESS; DESIGN;

D O I：

10.1016/j.amc.2014.10.089

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper investigates the global exponential stability of neural networks with discrete and distributed delays. A new criterion for the exponential stability of neural networks with mixed delays is derived by using the Lyapunov stability theory, Homomorphic mapping theory and matrix theory. The obtained result is easier to be verified than those previously reported stability results. Finally, some illustrative numerical examples are given to show the effectiveness of the proposed result. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：121 / 130

页数：10

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