New global stability criteria of neural networks with time delays

This paper studies global asymptotic stability of a general class of neural networks with time delays by utilizing Razumikhin theorem and the linear matrix inequality technique. Distinct difference from other analytical approaches lies in “linearization” of the neural network model, by which the considered neural network model is transformed into a linear time-variant system. New sufficient conditions ensuring global asymptotic stability of the unique equilibrium point of delayed neural networks are obtained. The obtained conditions show to be less conservative and restrictive than those reported in the literature. A numerical simulation is given to illustrate the validity of our results.