Topics in matrix analysis
Global stability for cellular neural networks with time delay
IEEE Transactions on Neural Networks
New criteria for globally exponential stability of delayed Cohen-Grossberg neural network
Mathematics and Computers in Simulation
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Global exponential stability of Cohen-Grossberg neural network with time varying delays
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
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This paper studies the global convergence properties of Cohen-Grossberg neural networks with multiple time delays. Without assuming the symmetry of interconnection weight coefficients, and the differentiability and boundedness 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 unique and globally asymptotically stable equilibrium point. Several examples are given to illustrate the advantages of our results over the previously reported results in the literature.