Global exponential convergence of Cohen-Grossberg neural networks with time delays

Authors:
Hongtao Lu;Ruiming Shen;Fu-Lai Chung
Affiliations:
Dept. of Comput. Sci. & Eng., Shanghai Jiao Tong Univ., China;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

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Abstract

In this paper, we derive a general sufficient condition ensuring global exponential convergence of Cohen-Grossberg neural networks with time delays by constructing a novel Lyapunov functional and smartly estimating its derivative. The proposed condition is related to the convex combinations of the column-sum and the row-sum of the connection matrices and also relaxes the constraints on the network coefficients. Therefore, the proposed condition generalizes some previous results in the literature.