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Identification of Auto-Regressive Exogenous Hammerstein Models Based on Support Vector Machine Regression | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models co...Show More

Abstract:

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.
Published in: IEEE Transactions on Control Systems Technology ( Volume: 21, Issue: 6, November 2013)
Page(s): 2083 - 2090
Date of Publication: 21 December 2012

ISSN Information:


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