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

被引：84
|
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
收藏
页码：2354 / 2370
页数：17
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