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Dynamic Event-Triggered H∞ State Estimation of Time-Delayed Neural Networks with Stochastic Parameters and Measurement Outliers

This paper is concerned with the dynamic event-triggered H∞ state estimation problem for a class of time-delayed neural networks subject to stochastic parameters and measurement outliers. A set of random variables is proposed to represent the random fluctuation of system parameters. The side-effects of measuring outliers are mitigated by using a certain confidence-dependent saturation function. For saving energy, the dynamic event-triggered mechanism is introduced to control signal trans-mission from the sensors to state estimators. The aim of the addressed problem is to design an estimator for the neural networks ensuring that the estimation error dynamics is exponentially mean-square stable and the H∞ disturbance rejection attenuation level is achieved. A sufficient condition of the expected estimator is given by using Lyapunov functional approach, and the gain matrix of the desired estimator is obtained by solving a certain matrix inequality. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed dynamic event-triggered state estimation scheme.