Global Exponential Stability Analysis for Uncertain Stochastic Neural Networks with Discrete and Distributed Time-Varying Delays

In this paper, the global exponential stability is investigated for a class of stochastic neural networks with both discrete and distributed delays and norm-bounded uncertainties. Based on Lyapunov stability theory and stochastic analysis approaches, delay-dependent criteria are derived to ensure the global, robust, exponential stability of the addressed system in the mean square for all admissible parameter uncertainties. The criteria can be checked easily by the LMI Control Toolbox in Matlab. A numerical example is given to illustrate the effectiveness and improvement over some existing results.