Dynamic event-triggered H∞ state estimation for discrete-time complex-valued memristive neural networks with mixed time delays

Abstract

This paper explores the $H_{\infty }$ state estimation problem for a category of discrete-time complex-valued memristive neural networks (CVMNNs). Regarding the studied CVMNNs, the phenomena of the distributed delay and time-varying delay are taken into account so as to describe the system more practically. Firstly, for further effective analysis, the examined CVMNNs are converted to an augmented system that integrates both the real and imaginary dynamics about the initial CVMNNs. To alleviate the communication burden, a representative dynamic event-triggered scheme is employed, for the first time, in the state estimator design of discrete-time CVMNNs. By establishing the Lyapunov functional, a sufficient condition is derived to assure the asymptotical stability of the estimation error system. Subsequently, the explicit expression of the desired estimator is obtained by resolving several matrix inequalities. Ultimately, the efficacy of the designed state estimator is substantiated through a simulation example.