New Exponential Passivity Analysis of Integro-Differential Neural Networks with Time-Varying Delays

Ninrat Promdee, Kanit Mukdasai, Prem Junsawang

Abstract

This paper aims to deal with the problems of exponential stability and exponential passivity analysis for integro-differential neural networks with time-varying delays, based on the mixed model transformation approach. In this work, we investigate both discrete and distributed time-varying delays for which the upper bounds are available. By constructing augments Lyapunov-Krasovskii functional and various inequalities, the new delay-dependent criterion is established and is mathematically expressed in terms of linear matrix inequalities (LMIs) to guarantee the exponential stability of the considered system. Furthermore, depended on the proof for the exponential stability of the system, the constructed delay-dependent method was derived from the exponential passivity for neural networks with mixed time-varying delays. Also, numerical examples are given to illustrate the effectiveness of the findings.