Passivity analysis of memristor-based recurrent neural networks with time-varying delays

被引：85

作者：

Wen, Shiping ^{[1
,2
]}

Zeng, Zhigang ^{[1
,2
]}

Huang, Tingwen ^{[3
]}

Chen, Yiran ^{[4
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China

[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China

[3] Texas A&M Univ Qatar, Doha 5825, Qatar

[4] Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15261 USA

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2013年 / 350卷 / 08期

基金：

高等学校博士学科点专项科研基金;

关键词：

GLOBAL EXPONENTIAL STABILITY; HETEROGENEOUS MULTIAGENT SYSTEMS; STRONG EXTERNAL STIMULI; ASYMPTOTIC STABILITY; MULTIPLICATIVE NOISES; DISTRIBUTED DELAYS; TRACKING CONTROL; SYNCHRONIZATION; UNCERTAINTY; DEVICE;

D O I：

10.1016/j.jfranklin.2013.05.026

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper investigates the delay-dependent exponential passivity problem of the memristor-based recurrent neural networks (RNNs). Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based RNNs is established. Taking into account of the information of the neuron activation functions and the involved time-varying delays, several improved results with less computational burden and conservatism have been obtained in the sense of Filippov solutions. A numerical example is presented to show the effectiveness of the obtained results. (C) 2013 The Franklin Institute, Published by Elsevier Ltd. All rights reserved.

引用

页码：2354 / 2370

页数：17

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