文献类型：期刊文献

英文题名：Lag Synchronization of Memristor-Based Coupled Neural Networks via omega-Measure

作者：Li, Ning[1,2];Cao, Jinde[3,4]

第一作者：Li, Ning;李宁

通讯作者：Li, N[1];Cao, J[2]

机构：[1]Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China;[2]Henan Univ Econ & Law, Coll Math & Informat Sci, Zhengzhou 450046, Peoples R China;[3]Southeast Univ, Dept Math, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China;[4]King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia

第一机构：Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China

通讯机构：[1]corresponding author), Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China;[2]corresponding author), Southeast Univ, Dept Math, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China.

年份：2016

卷号：27

期号：3

起止页码：686-697

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20154201386175);Scopus(收录号：2-s2.0-84943654081);WOS:【SCI-EXPANDED(收录号:WOS:000372022900015)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61573096 and Grant 61272530, in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK2012741, and in part by the 333 Engineering Foundation of Jiangsu Province of China under Grant BRA2015286.

语种：英文

外文关键词：Feedback control; lag synchronization; memristor-based coupled neural networks; parameters mismatch; transmittal delay

摘要：This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the omega-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.