State estimation using artificial neural networks

Abstract

The state estimation problem is addressed using artificial neural networks. The neural networks used are the adaptive linear combiner and a multilayer net. Training is performed by using several Kalman filter solutions to set the different weights. The derived neural network estimator gives state estimates when the system is subjected to unknown noises. Examples are given to illustrate the proposed approach.<>