Bayesian Estimation with Artificial Neural Network

Abstract

A nonlinear filter based on an artificial neural network (ANN) is proposed to accurately estimate the state of a nonlinear dynamic system. The ANN is trained to learn the nonlinear mapping between the inputs and outputs of training data. The proposed filter is computationally efficient for online applications because estimation error can be directly estimated once the ANN is trained offline. The unscented transformation (UT) is employed in this filter to approximate the first two moments of the estimate. Under the scenarios considered in this paper, it is shown through numerical simulation that the proposed filter leads to better performance than the extended Kalman filter (EKF), unscented Kalman filter (UKF), and a state-of-the-art nonlinear filter.