In this paper, stochastic interval Hopfield neural networks with time-varying delays are investigated. By applying the Razumikhin-type theorem as well as inequality technique, a set of novel sufficient criteria independent of delays are given for the exponential stability of such networks. As a by-product, for the deterministic Hopfield neural networks with time-varying delays, some delay-independent criteria for their global exponential robust stability are also obtained. The proposed results improve and extend them in the earlier literature and are easier to verify. A numerical example and simulation are also given to illustrate the effectiveness of our results.
This paper investigates the mean square stability of a class of stochastic neural networks with time-varying delays. By virtue of the stochastic analysis method and linear matrix inequality (LMI) approach, a new sufficient condition is proposed where the feasibility of the conditions can be readily checked by the Matlab LMI control toolbox. Moreover, our method has the advantage of removing the restrictions on the time varying delays, so the derived results are less conservative than the previous works. A numerical example with simulations are provided to illustrate the effectiveness of the developed results.