Covergence of stochastic fuzzy Cohen–Grossberg neural networks with reaction–diffusion and distributed delays

In this paper, a class of stochastic fuzzy Cohen–Grossberg neural networks with reaction–diffusion and distributed delays is discussed. Based on Lyapunov functionals, inequality analysis, Itô’s formula, nonnegative semimartingale convergence theorem and stochastic analysis, some stability sufficient conditions are presented to guarantee the neural networks to be almost sure and mean square exponentially stable, respectively. The proposed results improve and extend some earlier literature and are easier to verify. For illustration, an example is given to illustrate the feasibility of the results.