Synchronization of Delayed Memristor-Based Neural Networks via Pinning Control With Local Information

被引:4
|
作者
Yang, Zhanyu [1 ]
Zhao, Bo [2 ]
Liu, Derong [3 ,4 ]
机构
[1] South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China
[2] Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China
[3] Southern Univ Sci & Technol, Sch Syst Design & Intelligent Mfg, Shenzhen 518055, Peoples R China
[4] Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Asymptotical synchronization; memristor-based neural networks (MNNs); pinning control; time-delays; GLOBAL EXPONENTIAL SYNCHRONIZATION; FINITE-TIME SYNCHRONIZATION; LAG SYNCHRONIZATION;
D O I
10.1109/TNNLS.2023.3270345
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 article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice. To overcome this problem, a novel pinning control policy is developed to achieve synchronization of delayed MNNs, which depends only on local information of MNNs, for reducing communication and calculation burdens. Furthermore, sufficient conditions for synchronization of delayed MNNs are provided. Finally, numerical simulation and comparative experiments are conducted to verify the effectiveness and superiority of the proposed pinning control method.
引用
收藏
页码:13619 / 13630
页数:12
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