A delay partitioning approach to delay-dependent stability analysis for neutral type neural networks with discrete and distributed delays

被引：78

作者：

Lakshmanan, S. ^{[1
]}

Park, Ju H. ^{[1
]}

Jung, H. Y. ^{[1
]}

Kwon, O. M. ^{[2
]}

Rakkiyappan, R. ^{[3
]}

机构：

[1] Yeungnam Univ, Dept Elect Engn Informat & Commun Engn, Kyongsan 712749, South Korea

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

[3] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India

来源：

NEUROCOMPUTING | 2013年 / 111卷

关键词：

Neural networks; Delay partitioning approach; Stability; Neutral delay; GLOBAL EXPONENTIAL STABILITY; ASYMPTOTIC STABILITY; ROBUST STABILITY; TIME-DELAY; SYSTEMS; STABILIZATION; CRITERIA;

D O I：

10.1016/j.neucom.2012.12.016

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 stability analysis of neutral type neural networks with discrete and distributed delays. Some improved delay-dependent stability results are established by using a delay partitioning approach for the networks. By employing a new type of Lyapunov-Krasovskii functionals, new delay-dependent stability criteria are derived. All the criteria are expressed in terms of linear matrix inequalities (LMIs), which can be solved efficiently by using standard convex optimization algorithms. Finally, numerical examples are given to illustrate the less conservatism of the proposed method. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：81 / 89

页数：9

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