Online State--Space Modeling Using Recurrent Multilayer Perceptrons with Unscented Kalman Filter

Authors:
Jongsoo Choi;Tet Hin Yeap;Martin Bouchard
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada K1N 6N5;School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada K1N 6N5;School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada K1N 6N5
Venue:
Neural Processing Letters
Year:
2005

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Abstract

A nonlinear black-box modeling approach using a state--space recurrent multilayer perceptron (RMLP) is considered in this paper. The unscented Kalman filter (UKF), which was proposed recently and is appropriate for state--space representation, is employed to train the RMLP. The UKF offers a derivative-free computation and an easy implementation, compared to the extended Kalman filter (EKF) widely used for training neural networks. In addition, the UKF has a fast convergence rate and an excellent capability of parameter estimation which are appropriate for online learning. Through modeling experiments of nonlinear systems, the effectiveness of the RMLP trained with the UKF is demonstrated.