DocumentCode :
26033
Title :
Global Exponential Synchronization of Two Memristor-Based Recurrent Neural Networks With Time Delays via Static or Dynamic Coupling
Author :
Zhenyuan Guo ; Jun Wang ; Zheng Yan
Author_Institution :
Coll. of Math. & Econ., Hunan Univ., Changsha, China
Volume :
45
Issue :
2
fYear :
2015
fDate :
Feb. 2015
Firstpage :
235
Lastpage :
249
Abstract :
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.
Keywords :
Lyapunov methods; delays; memristors; recurrent neural nets; synchronisation; Lyapunov stability theory; Lyapunov-Krasovskii functionals; MRNN drive-response pair; anti-interference capacity; dynamic coupling; dynamic output coupling rule; dynamic state coupling rule; global exponential synchronization; memristor-based recurrent neural networks; static coupling; static output coupling rule; static state coupling rule; time delays; Couplings; Cybernetics; Delay effects; Mathematical model; Memristors; Recurrent neural networks; Synchronization; Memristor; recurrent neural networks; synchronization; time delay;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics: Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2168-2216
Type :
jour
DOI :
10.1109/TSMC.2014.2343911
Filename :
6877732
Link To Document :
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