Stop and Go adaptive strategy for synchronization of delayed memristive recurrent neural networks with unknown synaptic weights

被引：18

作者：

Ding, Sanbo ^{[1
]}

Wang, Zhanshan ^{[1
]}

Niu, Haisha ^{[1
]}

Zhang, Huaguang ^{[1
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Peoples R China

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2017年 / 354卷 / 12期

基金：

中国国家自然科学基金;

关键词：

GLOBAL EXPONENTIAL SYNCHRONIZATION; TIME-SYSTEMS; IDENTIFICATION;

D O I：

10.1016/j.jfranklin.2017.05.011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Although the drive-response synchronization problem of memristive recurrent neural networks (MRNNs) has been widely investigated, all the existing results are based on the assumption that the parameters of the drive system are known in prior, which are difficult to implement in real-life applications. In the present paper, a Stop and Go adaptive strategy is proposed to investigate the synchronization control of chaotic delayed MRNNs with unknown memristive synaptic weights. Firstly, by defining a series of measurable logical switching signals, a switched response system is constructed. Subsequently, by utilizing the logical switching signals, several suitable parameter update laws are proposed, then some different adaptive controllers are devised to guarantee the synchronization of unknown MRNNs. Since the parameter update laws are weighted by the logical switching signals, they will work or stop automatically with the switch of the unknown weights of drive system. Finally, two numerical examples with their computer simulations are provided to illustrate the effectiveness of the proposed adaptive synchronization schemes. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：4989 / 5010

页数：22

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