Global exponential stability of CohenGrossberg neural networks with distributed delays
In this paper, the globally exponential stability of CohenGrossberg neural networks with continuously distributed delays is investigated. New theoretical results are presented in the presence of external stimuli. It is shown that the CohenGrossberg 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.
cohengrossberg neural network distributed delays external stimuli exponentially stable