Exponential stability of stochastic MJSNNs with partly unknown transition probabilities

This paper investigates exponential stability of stochastic Markovian jumping static neural networks (MJSNNs) with mode-dependent time-varying delays and partly unknown transition probabilities. Based on the Lyapunov-Krasovskii functional method and stochastic analysis technique, some new stochastic stability criteria are derived to guarantee the exponential stability in mean square of Markovian jumping static neural networks in terms of linear matrix inequalities. A numerical example is provided to illustrate the efficiency of the main results obtained at the end.