Robust stability of stochastic Cohen–Grossberg neural networks with mixed time-varying delays

In the paper, the problem of robust exponential stability analysis is investigated for stochastic Cohen–Grossberg neural networks with both interval time-varying and distributed time-varying delays. By employing an augmented Lyapunov–Krasovskii functional, together with the LMI approach and definition on convex set, two delay-dependent conditions guaranteeing the robust exponential stability (in the mean square sense) of addressed system are presented. Additionally, the activation functions are of more general descriptions and the derivative of time-varying delay being less than 1 is released, which generalize and further improve those earlier methods. Numerical examples are provided to demonstrate the effectiveness of proposed stability conditions.