Stability for Neural Networks With Time-Varying Delays via Some New Approaches

被引：207

作者：

Kwon, Oh-Min ^{[1
]}

Park, Myeong-Jin ^{[1
]}

Lee, Sang-Moon ^{[2
]}

Park, Ju H. ^{[3
]}

Cha, Eun-Jong ^{[4
]}

机构：

[1] Chungbuk Natl Univ, Sch Elect Engn, Dept Elect Engn, Cheongju 361763, South Korea

[2] Daegu Univ, Dept Elect Engn, Div Elect Engn, Gyongsan 712714, South Korea

[3] Yeungnam Univ, Dept Elect Engn, Kyongsan 712749, South Korea

[4] Chungbuk Natl Univ, Dept Biomed Engn, Sch Med, Cheongju 361763, South Korea

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2013年 / 24卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

Lyapunov method; neural networks; stability; time-varying delays; EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; LKF APPROACH; CRITERIA; DISCRETE; SYSTEMS; PASSIVITY; DYNAMICS;

D O I：

10.1109/TNNLS.2012.2224883

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 considers the problem of delay-dependent stability criteria for neural networks with time-varying delays. First, by constructing a newly augmented Lyapunov-Krasovskii functional, a less conservative stability criterion is established in terms of linear matrix inequalities. Second, by proposing novel activation function conditions which have not been proposed so far, further improved stability criteria are proposed. Finally, three numerical examples used in the literature are given to show the improvements over the existing criteria and the effectiveness of the proposed idea.

引用

页码：181 / 193

页数：13

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