New delay-dependent stability criteria for uncertain stochastic neural networks with discrete interval and distributed delays

This paper studies the globally robustly asymptotical stability in mean square of uncertain stochastic neural networks with discrete interval and distributed time-varying delays. By constructing an augmented Lyapunov–Krasovskii functional, some delay-dependent criteria for the globally robustly asymptotical stability of such systems are formulated in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to illustrate the effectiveness of the obtained results.