Passivity criteria for continuous-time neural networks with mixed time-varying delays

Abstract

This paper is concerned with the problem of passivity analysis for uncertain continuous-time neural networks with mixed time-varying delays. The mixed time-varying delays consist of both discrete and distributed delays, in which the discrete delays are assumed to be varying within a given interval. In addition, the uncertainties are assumed to be norm-bounded. By employing a novel Lyapunov–Krasovskii functional, new passivity delay-interval-dependent criteria are established to guarantee the passivity performance. When estimating an upper bound of the derivative of the Lyapunov–Krasovskii functional, we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results. These passivity conditions are presented in terms of linear matrix inequalities, which can be easily solved via standard numerical software. Some numerical examples are given to illustrate the effectiveness of the proposed method.