New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays

摘要: This paper investigates the problem of passivity analysis for a class uncertain discrete-time stochastic neural networks with mixed time delays. Here delays are assumed to be discrete and distributed uncertainties time-varying norm-bounded parameter uncertainties. By constructing novel Lyapunov functional introducing some appropriate free-weighting matrices, delay-dependent criteria derived. Furthermore, additional useful terms about delay will handled by estimating upper bound derivative functionals, which is different from existing results. These can developed in frame convex optimization problems then solved via standard numerical software. Finally, example given demonstrate effectiveness proposed