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Synchronization of memristor-based delayed BAM neural networks with fractional-order derivatives
journal contributionposted on 2016-11-01, 00:00 authored by C Rajivganthi, F A Rihan, S Lakshmanan, R Rakkiyappan, P Muthukumar
This article deals with the problem of synchronization of fractional-order memristor-based BAM neural networks (FMBNNs) with time-delay. We investigate the sufficient conditions for adaptive synchronization of FMBNNs with fractional-order 0 < α < 1. The analysis is based on suitable Lyapunov functional, differential inclusions theory, and master-slave synchronization setup. We extend the analysis to provide some useful criteria to ensure the finite-time synchronization of FMBNNs with fractional-order 1 < α < 2, using Mittag-Leffler functions, Laplace transform, and linear feedback control techniques. Numerical simulations with two numerical examples are given to validate our theoretical results. Presence of time-delay and fractional-order in the model shows interesting dynamics. © 2016 Wiley Periodicals, Inc. Complexity 21: 412–426, 2016.
Pagination412 - 426
Publication classificationC1 Refereed article in a scholarly journal
Copyright notice2016 Wiley Periodicals, Inc.
Science & TechnologyPhysical SciencesMathematics, Interdisciplinary ApplicationsMultidisciplinary SciencesMathematicsScience & Technology - Other Topicsfractional-ordermemristor-based BAM neural networkssynchronizationtime-delaysGLOBAL EXPONENTIAL STABILITYPROJECTIVE SYNCHRONIZATIONPERIODIC-SOLUTIONEXISTENCECRITERIONComputation Theory and Mathematics