Robust stability criteria for uncertain stochastic neural networks with two time-varying delay components

This paper is concerned the robust stability analysis problem for uncertain stochastic neural networks with two time-varying delay components. By utilizing a Lyapunov-Krasovskii functional and conducting stochastic analysis, we show that the addressed neural networks are globally, robustly, asymptotically stable if a convex optimization problem is feasible. Some stability criteria are derived such that for all admissible uncertainties. And these stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Numerical examples are given to demonstrate the usefulness of the proposed robust stability criteria.