New Delay-Dependent Exponential Passivity Analysis of Neutral-Type Neural Networks with Discrete and Distributed Time-Varying Delays

Peerapongpat Singkibud, Narongsak Yotha, Kanit Mukdasai

Abstract

This work focuses on the problem of exponential passivity analysis for neutral-type neural networks with discrete and distributed time-varying delays by employing the mixed model transformation approach. The delays are discrete, neutral and distributed time-varying delays that the upper bounds for the time-varying delays are available. The restrictions on the derivatives of the distributed time-varying delays are removed, which mean that a fast distributed time-varying delay is allowed. Based on a appropriate Lyapunov-Krasovskii functional, application of zero equations and using various inequalities, such as the famous Jensen inequality, Wirtinger-based integral inequality, Peng-Park's integral inequality, etc. A novel delay-dependent criterion is established to ensure the exponential passivity of the systems considered. Moreover, the exponential passivity criterion is presented in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the superiority of the proposed method and capability of results over another research as compared with the least upper bounds of delay as well.