New results for global stability of cohen-grossberg neural networks with discrete time delays

Authors:
Zeynep Orman;Sabri Arik
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Istanbul University, Avcilar, Istanbul, Turkey;Department of Computer Engineering, Faculty of Engineering, Istanbul University, Avcilar, Istanbul, Turkey
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 5
Cited 0

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IEEE Transactions on Neural Networks

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Abstract

This paper studies the global convergence properties of Cohen-Grossberg neural networks with discrete time delays. Without assuming the symmetry of interconnection weight coefficients, and the monotonicity and differentiability of activation functions, and by employing Lyapunov functionals, we derive new delay independent sufficient conditions under which a delayed Cohen-Grossberg neural network converges to a globally asymptotically stable equilibrium point. Some examples are given to illustrate the advantages of the results over the previously reported results in the literature.