Online state-space modeling using recurrent multilayer perceptrons with unscented Kalman filter

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...

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Bibliographic Details
Published in:Neural processing letters 2005-08, Vol.22 (1), p.69-84
Main Authors: CHOI, Jongsoo, TET HIN YEAP, BOUCHARD, Martin
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Extended Kalman filter
Modelling
Multilayer perceptrons
Neural networks
Nonlinear systems
Parameter estimation
System effectiveness
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Summary: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.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-005-2157-2