Passivity Analysis of Stochastic Neural Networks with Mixed Time-Varying Delays

摘要

In this paper, the passivity problem is investigated for a class of stochastic neural networks with discrete time-varying delay and distributed time-varying delay as well as generalized activation functions. By constructing appropriate Lyapunov-Krasovskii functionals, and employing the free-weighting matrix method and stochastic analysis technique, a delay-dependent criterion for checking the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.