Neural network model adaptation and its application to process control

A multi-layer perceptron network is made adaptive by weight updating using the extended Kalman filter (EKF). When the network is used as a model for a non-linear plant, the model can be on-line adapted with input/output data to capture system time-varying dynamics and consequently used in adaptive c...

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Bibliographic Details
Published inAdvanced engineering informatics Vol. 18; no. 1; pp. 1 - 8
Main Authors Chang, T.K., Yu, D.L., Yu, D.W.
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 2004
Subjects
Adaptive neural networks
Continuous-stirred tank reactor processes
Extended Kalman filter
Model inversion
Model inversion
Continuous-stirred tank reactor processes
Extended Kalman filter
Adaptive neural networks
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Summary:A multi-layer perceptron network is made adaptive by weight updating using the extended Kalman filter (EKF). When the network is used as a model for a non-linear plant, the model can be on-line adapted with input/output data to capture system time-varying dynamics and consequently used in adaptive control. The paper describes how the EKF algorithm is used to update the network model and gives the implementation procedure. The developed adaptive model is evaluated for on-line modelling and model inversion control of a simulated continuous-stirred tank reactor. The modelling and control results show the effectiveness of model adaptation to system disturbance and a global tracking control.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1474-0346
1873-5320
DOI:10.1016/j.aei.2004.01.001