Global Exponential Synchronization of Two Memristor-Based Recurrent Neural Networks with Time Delays via Static or Dynamic Coupling

被引：164
作者：
Guo, Zhenyuan [1 ,2 ]
Wang, Jun [2 ]
Yan, Zheng [2 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2015年 / 45卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Memristor; recurrent neural networks; synchronization; time delay; ADAPTIVE SYNCHRONIZATION; CHAOTIC SYSTEMS; STABILITY; CIRCUIT; NEURONS; SYNAPSE; ARRAY;
D O I：
10.1109/TSMC.2014.2343911
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 is concerned with the global exponential synchronization of two memristor-based recurrent neural networks (MRNNs) with time delays via static or dynamic coupling. First, four coupling rules (i.e., static state coupling, static output coupling, dynamic state coupling, and dynamic output coupling) are designed for the exponential synchronization of drive-response pair of MRNNs. Then, several global exponential synchronization criteria are derived by constructing suitable Lyapunov-Krasovskii functionals based on the Lyapunov stability theory. Compared with existing results on synchronization of MRNNs, the conditions herein are easy to be verified. Moreover, the designed dynamic state coupling and output coupling rules have good anti-interference capacity. Finally, two illustrative examples are presented to substantiate the effectiveness and characteristics of the presented theoretical results.
引用
收藏
页码：235 / 249
页数：15
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