Delay-range-dependent passivity analysis for uncertain stochastic neural networks with discrete and distributed time-varying delays

The purpose of this paper is to investigate the problem of passivity analysis for delayed uncertain stochastic neural networks (DUSNNs) with discrete and distributed time-varying delays. The novelty of this paper lies in the consideration of a new integral inequality proved to be less conservatism than celebrated Jensen׳s inequality and takes fully the relationship between the terms in the Leibniz-Newton formula within the framework of linear matrix inequalities (LMIs). By constructing a suitable Lyapunov-Krasovskii functional with triple integral terms and using Jensen׳s inequality, integral inequality technique and linear matrix inequality frame work, which guarantees stability for the passivity of addressed neural networks. This LMI can be easily solved via convex optimization techniques. Using several examples from the literature, it is shown that the proposed stabilization theorem is less conservative than previous results. Finally, the technique is applied to benchmark problem, showing how to derive efficient stability criteria for realistic problems, using the proposed technique.