Switching-Like Event-Triggered State Estimation for Reaction–Diffusion Neural Networks Against DoS Attacks

Abstract

In this paper, event-triggered state estimation for reaction–diffusion neural networks (RDNNs) subject to Denial-of-Service (DoS) attacks is investigated. A switching-like event-triggered strategy (SETS) is proposed to handle intermittent DoS attacks, meanwhile, alleviate the burden of the network while preserving the accepted performance of the considered systems. Moreover, to obtain the unknown state, the corresponding state estimator of RDNNs is constructed. Furthermore, by virtue of a piecewise Lyapunov–Krasovskii functional method, sufficient conditions are obtained to ensure the exponential stability of the closed-loop systems. Finally, a numerical simulation is provided to demonstrate the feasibility and advantages of the obtained results.