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.