Global dissipativity of uncertain discrete-time stochastic neural networks with time-varying delays

In this letter, the problem on global exponential dissipativity in mean is investigated for uncertain discrete-time stochastic neural networks with time-varying delays. In the concerned model, the stochastic disturbance is described by the Brownian motion, and the time-varying delays in a given range. By constructing appropriate Lyapunov–Krasovskii functional (LKF), combining with Jensen's inequality, stochastic analysis method and the free-weighting matrix method, a new delay-dependent global dissipativity criteria is obtained in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in Matlab. Finally, three examples are exploited to show the usefulness of the results derived.