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    Extended and Unscented Kalman Filters for Artificial Neural Network Modelling of a Nonlinear Dynamical System

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    Fulltext not available
    Authors
    Saptoro, Agus
    Date
    2012
    Type
    Journal Article
    
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    Citation
    Saptoro, Agus. 2012. Extended and Unscented Kalman Filters for Artificial Neural Network Modelling of a Nonlinear Dynamical System. Theoretical Foundations of Chemical Engineering 46 (3): pp. 274-278.
    Source Title
    Theoretical Foundations of Chemical Engineering
    DOI
    10.1134/S0040579512030074
    ISSN
    0040-5795
    URI
    http://hdl.handle.net/20.500.11937/45184
    Collection
    • Curtin Research Publications
    Abstract

    Recently, artificial neural networks, especially feedforward neural networks, have been widely used for the identification and control of nonlinear dynamical systems. However, the determination of a suitable set of structural and learning parameter value of the feedforward neural networks still remains a difficult task. This paper is concerned with the use of extended Kalman filter and unscented Kalman filter based feed forward neural networks training algorithms. The comparisons of the performances of both algorithms are discussed and illustrated using a simulated example. The simulation results show that in terms of mean squared errors, unscented Kalman filter algorithm is superior to the extended Kalman filter and backpropagation algorithms since there are improvements between 2.45–21.48% (for training) and 8.35–29.15% (for testing). This indicates that unscented Kalman filter based feedforward neural networks learning could be a good alternative in artificial neural network models based applications for nonlinear dynamical systems.

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