Delay-dependent exponential stability for a class of stochastic neural networks with distributed delays and polytopic uncertainties

The global robust exponential stability in mean square for a class of stochastic neural networks with distributed delays and polytopic uncertainties is investigated in this paper. Parameter-dependent Lypaunov-Krasovskii functionals and free-weighting matrices are employed to obtain sufficient condition that guarantee the robust global exponential stability the considered stochastic neural networks. The derived sufficient conditions are proposed in terms of a set of relaxed linear matrix inequalities (LMIs), which can be checked easily by recently developed algorithms solving LMIs. A numerical example is given to demonstrate the effectiveness of the proposed criteria.