Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays

Abstract

Listen

Translate article

In this paper, we address robust passivity analysis for a class of uncertain neural networks (NNs) with discrete and distributed time-varying delays. By selecting an improved Lyapunov-Krasovskii functional (LKF) with a novel delay-produce-type (DPT) term and combing free-matrix-based (FMB) integral inequality, some sufficient criteria are obtained to guarantee the passivity of uncertain NNs. Then, the maximal allowable upper bound (MAUB) of time-varying delay can be obtained by reciprocally convex combination (RCC) technique through solving a group of linear matrix inequalities (LMIs). Finally, numerical examples are considered to illustrate the benefit and superiority of the method proposed.