Novel delay-probability-distribution-dependent mean square stability analysis for stochastic neural networks

In this paper, the delay-probability-distribution-dependent stability problem for stochastic neural networks with various time-varying delays is investigated. By considering a new Lyapunov-Krasovskii functional (LKF) concluding more delay-partitioning term and combining free weight matrices and stochastic processing techniques, an improved delay-probability-distribution-dependent condition is built in terms of linear matrix inequality (LMI) so that the system is mean square stable. Finally, a numerical example is given to verify the effectiveness the proposed criterion.