Reliable stabilization for memristor-based recurrent neural networks with time-varying delays

被引：41

作者：

Mathiyalagan, K. ^{[1
]}

Anbuvithya, R. ^{[2
]}

Sakthivel, R. ^{[3
]}

Park, Ju H. ^{[1
]}

Prakash, P. ^{[2
]}

机构：

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

[2] Periyar Univ, Dept Math, Salem 636011, India

[3] Sungkyunkwan Univ, Dept Math, Suwon 440746, South Korea

来源：

NEUROCOMPUTING | 2015年 / 153卷

基金：

新加坡国家研究基金会;

关键词：

Memristive neural networks; Reliable control; Stabilization; Wirtinger's inequality; EXPONENTIAL STABILITY; DISSIPATIVE CONTROL; SUSPENSION SYSTEMS; CRITERIA;

D O I：

10.1016/j.neucom.2014.11.043

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a general class of memristive recurrent neural networks with time-varying delays is considered. Based on the knowledge of memristor and recurrent neural networks (RNNs), a model of memristive based RNNs is established. After that the problem of reliable stabilization is studied by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using linear matrix inequality (LMI) framework. By use of the Wirtinger-type inequality, sufficient conditions are presented for the existence of a reliable state feedback controller, which can guarantee the global asymptotic stability of the memristive RNNs. Finally, an example is given to illustrate the theoretical results via numerical simulations. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：140 / 147

页数：8

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