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

The most important characteristics required in dynamic systems modeling using neural networks are fast convergence and generalization capability. To achieve these, this paper presents an approach to nonlinear state-space modeling using recurrent multilayer perceptrons (RMLP) trained with the unscent...

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Bibliographic Details
Main Authors: Jongsoo Choi, Tet Hin Yeap, Bouchard, M.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Computer science
control theory
systems
Control theory. Systems
Electronic mail
Exact sciences and technology
Feedback loop
Information technology
Modeling
Multilayer perceptrons
Neural networks
Parameter estimation
Signal processing algorithms
Speech analysis
State feedback
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Summary:The most important characteristics required in dynamic systems modeling using neural networks are fast convergence and generalization capability. To achieve these, this paper presents an approach to nonlinear state-space modeling using recurrent multilayer perceptrons (RMLP) trained with the unscented Kalman filter (UKF). The recently proposed UKF, which is proper to state-space representation, offers not only fast convergence but also derivative-free computations and an easy implementation, compared with the extended Kalman filter (EKF) widely used for neural networks. Through modeling experiments of nonlinear systems, the effectiveness of the RMLP with the UKF is demonstrated.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1400872