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

Abstract

In this paper, the globally exponential stability of Cohen-Grossberg neural networks with continuously distributed delays is investigated. New theoretical results are presented in the presence of external stimuli. It is shown that the Cohen-Grossberg neural network is globally exponentially stable, if the absolute value of the input vector exceeds a criterion. Comparison between our results and the previous results admits that our results have an extended application. A numerical example is supplied to illustrate the effectiveness of our approach. © 2008 Elsevier B.V. All rights reserved.