Delay-probability-distribution-dependent stability criteria for discrete-time stochastic neural networks with random delays

The problem of delay-probability-distribution-dependent robust stability for a class of discrete-time stochastic neural networks (DSNNs) with delayed and parameter uncertainties is investigated. The information of the probability distribution of the delay is considered and transformed into parameter matrices of the transferred DSSN model. In the DSSN model, the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By constructing an augmented Lyapunov-Krasovskii functional and introducing some analysis techniques, some novel delay-distribution-dependent mean square stability conditions for the DSSN, which are to be robustly globally exponentially stable, are derived. Finally, a numerical example is provided to demonstrate less conservatism and effectiveness of the proposed methods.