Identification of Auto-Regressive Exogenous Hammerstein Models Based on Support Vector Machine Regression

This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models consisting of a SVM, which models the static nonlinearity, followed by an ARX representation of the linear element. The model parame...

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Bibliographic Details
Published in:IEEE transactions on control systems technology 2013-11, Vol.21 (6), p.2083-2090
Main Authors: Al-Dhaifllah, Mujahed, Westwick, David T.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Autoregressive processes
Computer simulation
Control systems
Cost function
Hammerstein
identification
Least squares approximation
Mathematical models
Regression
Representations
Robustness
Support vector machines
support vector machines (SVMs)
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Summary:This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models consisting of a SVM, which models the static nonlinearity, followed by an ARX representation of the linear element. The model parameters can be estimated by minimizing an ε-insensitive loss function, which can be either linear or quadratic. In addition, the value of the uncertainty level, ε, can be specified by the user, which gives control over the sparseness of the solution. The effects of these choices are demonstrated using both simulated and experimental data.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2012.2228193