Passivity analysis of discrete-time stochastic neural networks with time-varying delays

Abstract

In this paper, the problem of passivity analysis is investigated for a class of discrete-time stochastic neural networks with time-varying delays. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov–Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, a delay-dependent passivity condition is derived in terms of linear matrix inequalities (LMIs). Furthermore, when the parameter uncertainties appear in the discrete-time stochastic neural networks with time-varying delays, a delay-dependent robust passivity condition is also presented. An example is given to show the effectiveness of the proposed criterion.