Improved asymptotic stability conditions for neural networks with discrete and distributed delays

被引：16

作者：

Chen, Yonggang ^{[1
,2
]}

Fei, Shumin ^{[1
]}

Zhang, Kanjian ^{[1
]}

机构：

[1] Southeast Univ, Minist Educ, Sch Automat, Key Lab Measurement & Control CSE, Nanjing 210096, Jiangsu, Peoples R China

[2] Henan Inst Sci & Technol, Dept Math, Xinxiang 453003, Peoples R China

来源：

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2012年 / 89卷 / 15期

关键词：

asymptotic stability; neural networks; discrete and distributed delays; augmented Lyapunov functional; linear matrix inequality; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; DEPENDENT STABILITY; ASSOCIATIVE MEMORY; MULTIPLE DISCRETE; CRITERIA; INTERVAL; SYSTEMS; DESIGN;

D O I：

10.1080/00207160.2012.695016

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper considers the asymptotic stability problem for a class of neural networks with discrete and distributed delays. Based on a new augmented Lyapunov functional and integral inequalities, the new asymptotic stability condition is established in terms of linear matrix inequality. Meanwhile, the importance of some augmented terms in the Lyapunov functional are discussed. Compared with previous methods to deal with the distributed delay, our method is less conservative due to the use of the new Lyapunov functional. Finally, numerical examples illustrate the relaxation of obtained results and our claims.

引用

页码：1938 / 1951

页数：14

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