Stability and synchronization of memristor-based fractional-order delayed neural networks.

Authors :: Chen L
Wu R
Cao J
Liu JB
Source :: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2015 Nov; Vol. 71, pp. 37-44. Date of Electronic Publication: 2015 Jul 31.
Publication Year :: 2015
Abstract: Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results.<br /> (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Subjects :: Algorithms
Equipment Design
Feedback
Linear Models
Nonlinear Dynamics
Neural Networks, Computer

Language :: English
ISSN :: 1879-2782
Volume :: 71
Database :: MEDLINE
Journal :: Neural networks : the official journal of the International Neural Network Society
Publication Type :: Academic Journal
Accession number :: 26282374
Full Text :: https://doi.org/10.1016/j.neunet.2015.07.012

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