NDLI: A class of delayed BAM self-adaptive neural network with uncertain parameters for projective synchronization problem

Content Provider	IEEE Xplore Digital Library
Author	Fenhan Wang Wuneng Zhou Yongqing Ran
Copyright Year	2011
Description	Author affiliation: College of Information Science and Technology, Donghua University, Shanghai 201620 China (Fenhan Wang; Wuneng Zhou; Yongqing Ran)
Abstract	This paper discusses a class of delayed Bidirectional Associate Memory (BAM for short) neural network in projective synchronization under uncertain parameters problem. Basing on BAM classic models and the Lyapunov stability theory, the thesis designs a new self-adaptive control method pointing at the characteristics of neural network. This method which other articles rarely mention can be realized in BAM system parameter identification and implementation of multidimensional projective synchronization of master-slave system. Through the numerical simulation, feasibility of this method has been justified.
Starting Page	481
Ending Page	485
File Size	434521
Page Count	5
File Format	PDF
ISBN	9781424499502
ISSN	21579563
e-ISBN	9781424499533
DOI	10.1109/ICNC.2011.6022079
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Chaos Delayed Bi-directional Associate Memory Neural Networks Projective Synchronization Parameter estimation Lyapunov Stability Theroy Neural networks Numerical simulation Parameter Identification Synchronization Mathematical model Equations
Content Type	Text
Resource Type	Article

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