Delay-Dependent Exponential Stability Analysis for Delayed Stochastic Hopfield Neural Networks

This paper is concerned with the problem of delay-dependent exponential stability analysis for a class of stochastic Hopfield type neural networks with constant time delays. By employing an augmented Lyapunov-Krasovskii functional, together with the linear matrix inequality approach, a delay-dependent condition guaranteeing the global exponential stability (in the mean square sense) of the considered stochastic neural network is presented. A numerical example is provided to demonstrate the effectiveness of the proposed stability condition.