Stability analysis of recurrent neural networks with interval time-varying delay via free-matrix-based integral inequality

被引：29

作者：

Lin, Wen Juan ^{[1
]}

He, Yong ^{[1
]}

Zhang, Chuan-Ke ^{[1
,2
]}

Wu, Min ^{[1
]}

Ji, Meng-Di ^{[3
]}

机构：

[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China

[2] Univ Liverpool, Dept Elect Engn & Elect, Liverpool L69 3GJ, Merseyside, England

[3] Cent South Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 205卷

基金：

中国国家自然科学基金;

关键词：

Recurrent neural networks; Interval time-varying delay; Stability; Augmented Lyapunov-Krasovskii functional; Free-matrix-based integral inequality; DEPENDENT STABILITY; ASYMPTOTIC STABILITY; DISCRETE; CRITERIA; SYSTEMS;

D O I：

10.1016/j.neucom.2016.04.052

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 recurrent neural networks with an interval time varying delay. A new Lyapunov-Krasovskii functional (LKF) containing some augmented double integral and triple integral terms is constructed, in which the information of the activation function and the lower bound of the delay are both fully considered. Then, a free-matrix-based integral inequality is employed to deal with the derivative of the LKF such that an improved stability criterion is derived. Finally, two numerical examples are provided to illustrate the effectiveness and the benefit of the proposed stability criterion. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：490 / 497

页数：8

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