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

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.