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Global exponential stability of Cohen-Grossberg neural networks with variable delays

Global exponential stability of Cohen-Grossberg neural networks with variable delays A class of generalized Cohen-Grossberg neural networks(CGNNs) with variable delays are investigated. By introducing a new type of Lyapunov functional and applying the homeomorphism theory and inequality technique, some new conditions are derived ensuring the existence and uniqueness of the equilibrium point and its global exponential stability for CGNNs. These results obtained are independent of delays, develop the existent outcome in the earlier literature and are very easily checked in practice. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics-A Journal of Chinese Universities Springer Journals

Global exponential stability of Cohen-Grossberg neural networks with variable delays

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Publisher
Springer Journals
Copyright
Copyright © 2009 by Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag GmbH
Subject
Mathematics; Applications of Mathematics; Mathematics, general
ISSN
1005-1031
eISSN
1993-0445
DOI
10.1007/s11766-009-1998-2
Publisher site
See Article on Publisher Site

Abstract

A class of generalized Cohen-Grossberg neural networks(CGNNs) with variable delays are investigated. By introducing a new type of Lyapunov functional and applying the homeomorphism theory and inequality technique, some new conditions are derived ensuring the existence and uniqueness of the equilibrium point and its global exponential stability for CGNNs. These results obtained are independent of delays, develop the existent outcome in the earlier literature and are very easily checked in practice.

Journal

Applied Mathematics-A Journal of Chinese Universities – Springer Journals

Published: Jun 10, 2009

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