Event-Triggered State Estimation for T–S Fuzzy Neural Networks with Stochastic Cyber-Attacks

Abstract

This paper is mainly concerned with event-triggered state estimation for Takagi–Sugeno (T–S) fuzzy neural networks subjected to stochastic cyber-attacks. An event-triggered scheme is utilized to decide whether the sampled data should be delivered or not. By taking the influence of the cyber-attacks into consideration, a T–S fuzzy model for the state estimation of neural networks is established with the event-triggered scheme. Through the utilization of Lyapunov stability theory and linear matrix inequality (LMI) techniques, the sufficient conditions are derived which can ensure the stability of estimator error systems. In addition, the gains of the estimator are acquired in the form of LMIs. Finally, a simulated example is presented to illustrate the effectiveness of the proposed method.