Event-triggered H∞ state estimation for discrete-time delayed switched stochastic neural networks with persistent dwell-time switching regularities and sensor saturations

In this paper, the H-infinity state estimation problem is considered for a class of the discrete-time delayed switched stochastic neural networks with sensor saturations. In order to improve the resource utilization efficiency, an event-triggered H-infinity state estimator is designed to regulate the transmission of the measurement outputs. The persistent dwell-time switching strategy is adopted in this paper which is more general and less conservative. By utilizing a proper Lyapunov-Krasovskii functional, some criteria are presented to ensure the exponential mean square stability of the estimation error system and the pre-specified H-infinity level of disturbance attenuation. Finally, a numerical example is given to illustrate the effectiveness of our results. (C) 2021 Published by Elsevier B.V.

Automated summary

This paper investigates the H-infinity state estimation problem for a class of discrete-time delayed switched stochastic neural networks with sensor saturations. An event-triggered state estimator is designed to improve resource utilization efficiency, and a persistent dwell-time switching strategy is adopted. Criteria are presented to ensure the stability of the estimation error system and disturbance attenuation.