Global exponential robust stability of stochastic reaction-diffusion static neural networks with mixed time delays

In this paper, a class of stochastic reaction-diffusion static neural networks with mixed time delays is investigated. Lyapunov stability theory combining with stochastic analysis approaches are employed to derive sufficient criteria ensuring the system to be globally exponentially robustly stable in the mean square. Finally, a numerical example is given to demonstrate the effectiveness and conservativeness of our theoretical results.