Design of Variance-Constrained H∞ State Estimation Algorithm for Delayed Memristive Neural Networks with Attacks: An Adaptive Event-Triggered Approach

Abstract

This paper studies the algorithm design of variance-constrained $H_{\infty}$ state estimation problem for delayed memristive neural networks with adaptive event-triggered mechanism. The denial-of-service attacks are modeled by a series of random variables obeying the Bernoulli distribution with known probability. In addition, the adaptive event-triggered mechanism is introduced into the sensor-to-estimator to avoid unnecessary resource consumption. Our purpose is to construct a finite-horizon state estimation algorithm, and sufficient condition is obtained for the estimation error system satisfying the $H_{\infty}$ performance requirement and the error variance boundedness. Finally, a numerical example is used to illustrate the feasibility of the presented $H_{\infty}$ state estimation algorithm.