Event-based nonfragile state estimation for memristive neural networks with multiple time-delays and sensor saturations
Xiaoguang Shao, Jie Zhang, Ming Lyu, Yanjuan Lu
Abstract
This article investigates the issue of nonfragile state estimation (SE) for memristor-based neural networks (MNNs) with leakage delay and proportional delay. In actual engineering, a multitude of usefulness data are transmitted to the estimator through the networks, which stress the burden on communication bandwidth. A dynamic event-triggered mechanism (DETM) that relies on incomplete measurements is utilised to select valuable data. A novel delay-dependent criterion for the existence of the event-based state estimator is derived in terms of a convex optimisation problem by means of the Lyapunov theory and some integer inequalities technique. In the end, two numerical simulations are shown to illustrate the validity of the proposed theoretical methods.