Robust Stability Analysis of Uncertain Stochastic Neural Networks with Time-Varying Delays

This paper is concerned with stochastic robust stability of a class of stochastic neural networks with time varying delays and parameter uncertainties. The parameter uncertainties are time-varying and norm-bounded. Based on Lyapunov-Krasovskii functional and stochastic analysis approaches, new stability criteria are presented in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural network to be robustly stochastically asymptotically stable in the mean square for all admissible uncertainties. Numerical examples are given to demonstrate the usefulness of the proposed robust stability criteria.