Improved stochastic dissipativity of uncertain discrete-time neural networks with multiple delays and impulses

This paper investigates the problem of global dissipativity and global exponential dissipativity for a class of uncertain discrete-time stochastic neural networks with multiple time-varying delays. Here the multiple time-varying delays are assumed to be discrete and distributed and the uncertainties are assumed to be time-varying norm-bounded parameter uncertainties. By choosing a novel Lyapunov functional, combining with linear matrix inequality technique (LMI), Jensen’s inequality and stochastic analysis method, a new delay-dependent global dissipativity criterion is obtained in the form of LMI, which can be easily verified numerically using the effective LMI toolbox in Matlab. One important feature presents in our paper is that without employing model transformation and free weighting matrices our obtained result leads to less conservatism. Two illustrative examples are given to show the usefulness of the obtained dissipativity conditions.