Existence and Exponential Stability of Periodic Solution for a Class of Generalized Neural Networks with Arbitrary Delays

By the continuation theorem of coincidence degree and M-matrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous...

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Bibliographic Details
Published in:Abstract and applied analysis 2010, Vol.2009 (2009), p.1-15
Main Authors: Zhang, Yimin, Li, Yongkun, Ye, Kuohui
Format: Article
Language:English
Online Access:Get full text
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Summary:By the continuation theorem of coincidence degree and M-matrix theory, we obtain some sufficient conditions for the existence and exponential stability of periodic solutions for a class of generalized neural networks with arbitrary delays, which are milder and less restrictive than those of previous known criteria. Moreover our results generalize and improve many existing ones.
ISSN:1085-3375
1687-0409