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Global existence and exponential stability of periodic solutions for recurrent neural networks with functional delay
Author(s) -
Yang Zhihui
Publication year - 2007
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.862
Subject(s) - uniqueness , mathematics , coincidence , exponential stability , simple (philosophy) , recurrent neural network , class (philosophy) , stability (learning theory) , artificial neural network , control theory (sociology) , degree (music) , mathematical analysis , computer science , nonlinear system , artificial intelligence , medicine , philosophy , physics , alternative medicine , control (management) , epistemology , pathology , quantum mechanics , machine learning , acoustics
Abstract This paper proposes a class of more general model of recurrent neural networks with functional delay, which has been found more suitable to apply directly. Simple and easily checkable conditions of existence, uniqueness, and global exponential stability of periodic solution for the recurrent neural network equations are obtained, utilizing the Lyapunov functional method and the theory of coincidence degree. Some known results are generalized and improved. Copyright © 2007 John Wiley & Sons, Ltd.