Robust stability of stochastic neural networks of neutral type with time-varying delays

This paper focuses on the global delay-dependent robust asymptotic stability of stochastic neural networks of neutral type with time-varying delays. The delay functions of networks under consideration are bounded but not necessarily differentiable. Based on the stochastic Lyapunov stability theory, itÔ´s differential rule and linear matrix inequality (LMI) optimization technique, a delay-dependent asymptotic stability criterion is derived. Finally, an illustrative example is given to show the effectiveness and feasibility of the proposed method.