Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10352
Title: Global exponential convergence of Cohen-Grossberg neural networks with time delays
Authors: Lu, H
Shen, R
Chung, FL 
Keywords: Cohen-Grossberg neural networks
Global exponential stability
Time delay
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks, 2005, v. 16, no. 6, p. 1694-1696 How to cite?
Journal: IEEE transactions on neural networks 
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.
URI: http://hdl.handle.net/10397/10352
ISSN: 1045-9227
DOI: 10.1109/TNN.2005.853336
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