Robustly Exponential Stability Analysis for Discrete Time Stochastic Neural Networks with Interval Time Varying Delays
This paper deals with the problems of the global exponential stability and stabilization for a class of uncertain discrete time stochastic neural networks with interval time varying delay. By using the linear matrix inequality method and the free weighting matrix technique, we construct a new Lyapunov Krasovskii functional and establish new sufficient conditions to guarantee that the uncertain discrete time stochastic neural networks with interval time varying delay are globally exponential stable in the mean square. Furthermore, we extend our consideration to the stabilization problem for a class of discrete time stochastic neural networks. Based on the state feedback control law, some novel delay dependent criteria of the robust exponential stabilization for a class of discrete time stochastic neural networks with interval time varying delay are established. The controller gains are designed to ensure the global robust exponential stability of the closed loop systems. Finally, numerical examples illustrate the effectiveness of the theoretical results we have obtained.
Neural Processing Letters