New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks

Abstract EN

The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay is investigated.

The delay-dependent sufficient criteria are derived in terms of linear matrix inequalities (LMIs).

The results are shown to be generalization of some previous results and are less conservative than the existing works.

Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved.

Two numerical examples are given to show the effectiveness and the benefits of the proposed method.