Event-based state estimation for delayed neural network systems with quantization

Abstract

This paper presents an investigation of the state estimation problem for a class of delayed neural network systems with event-triggered communication and quantization. The network bandwidth burden is reduced by using both an event-triggered communication scheme and quantization with which the state estimator design for delayed neural network systems is concerned. Considering the influence of the communication network, an event-based state estimator error dynamic model for delayed neural network systems is firstly constructed by taking the effect of the event-triggered scheme and quantization into consideration. Then by employing the Lyapunov functional approach and the linear matrix inequality technique, some sufficient conditions are obtained under which the state estimator exists and the estimator error dynamics is asymptotically stable. Finally, a numerical example is provided to demonstrate the usefulness of the proposed approach.