Exponential passivity for uncertain neural networks with time-varying delays based on weighted integral inequalities

This paper is concerned with the problem of an exponential passivity analysis for uncertain neural networks with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii functional and using the weighted integral inequality techniques to estimate its derivative. We established a sufficient criterion such that, for all admissible parameter uncertainties, the neural network is exponentially passive. The derived criteria are expressed in the terms of linear matrix inequalities (LMIs), that can be easily checked by using the standard numerical software. Illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed results.