Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks

•This paper investigates the mean square delay dependent - probability - distribution stability.•New Lyapunov-Krasovskii functional and stochastic analysis approach are used.•A novel sufficient condition is obtained in the form of linear matrix inequality.•Asymptotically stable in the mean-square sense is obtained for all admissible uncertainties.•Numerical examples are given to show the effectiveness of the proposed method.

The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov-Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.