On robust stability of uncertain stochastic neural networks with distributed and interval time-varying delays

This paper is concerned with the robust asymptotic stability analysis problem for stochastic uncertain neural networks with distributed and interval time-varying delays. By using the stochastic analysis approach, employing some free-weighting matrices and introducing an appropriate type of Lyapunov functional which take into account the ranges of delays, some new delay-range-dependent and rate-dependent stability criteria are established in terms of linear matrix inequalities (LMIs) to guarantee the delayed neural networks to be robustly asymptotically stable in the mean square. And the new criteria are applicable to both fast and slow time-varying delays. Three numerical examples have also been used to demonstrate the usefulness of the main results.