This article considers the state estimation problem for fractional-order neural networks subject to external disturbances using event-triggered state observers. For the first time, a novel method based on a discrete-time event-triggered mechanism and a nonlinear fractional-order state observer is obtained. A new discrete-time event-triggered mechanism is first proposed. Then a new nonlinear fractional-order state observer based on the newly discrete-time event-triggered mechanism is designed to estimate state vectors of the fractional-order neural networks robustly. The discrete-time event-triggered mechanism is Zeno-free, and it helps reduce unnecessary continuous signal transmission. A fractional-order-dependent condition to guarantee the existence of the discrete-time event-triggered nonlinear fractional-order state observer is established. This condition is translated into a convex optimization problem, which can be solved easily by MATLAB. The solution to this problem minimizes the attenuation levels and thus reduces the error in estimating the state vector of the fractional-order neural networks. The effectiveness of the proposed method is illustrated by two numerical examples and simulation results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.