Skip to main navigation Skip to search Skip to main content

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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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.
Original languageEnglish
Article number6877732
Pages (from-to)235-249
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume45
Issue number2
Online published13 Aug 2014
DOIs
Publication statusPublished - Feb 2015
Externally publishedYes

Research Keywords

  • Memristor
  • recurrent neural networks
  • synchronization
  • time delay

Fingerprint

Dive into the research topics of 'Global Exponential Synchronization of Two Memristor-Based Recurrent Neural Networks with Time Delays via Static or Dynamic Coupling'. Together they form a unique fingerprint.

Cite this