Synchronization control of memristor-based recurrent neural networks with perturbations

被引：93
|
作者：
Wang, Weiping [1 ]
Li, Lixiang [2 ]
Peng, Haipeng [2 ,3 ]
Xiao, Jinghua [1 ]
Yang, Yixian [2 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[3] Hangzhou Dianzi Univ, Zhejiang Prov Key Lab Data Storage & Transmiss Te, Hangzhou 310018, Zhejiang, Peoples R China
来源：
NEURAL NETWORKS | 2014年 / 53卷
基金：
中国博士后科学基金; 中国国家自然科学基金;
关键词：
Memristor-based recurrent neural networks; Synchronization control; Impulsive perturbation; Boundary perturbation;
D O I：
10.1016/j.neunet.2014.01.010
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, the synchronization control of memristor-based recurrent neural networks with impulsive perturbations or boundary perturbations is studied. We find that the memristive connection weights have a certain relationship with the stability of the system. Some criteria are obtained to guarantee that memristive neural networks have strong noise tolerance capability. Two kinds of controllers are designed so that the memristive neural networks with perturbations can converge to the equilibrium points, which evoke human's memory patterns. The analysis in this paper employs the differential inclusions theory and the Lyapunov functional method. Numerical examples are given to show the effectiveness of our results. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved,
引用
收藏
页码：8 / 14
页数：7
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