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System identification techniques based on support vector machines without bias term
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DOI：10.1002/acs.2404.png)
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The intention of this article is to utilize support vector machines (SVMs) as process models, which are the basis for most controller designs as well as simulation and monitoring tasks. SVMs are data-driven models comparable with regularization networks, which merge elements from robust statistics, statistical learning, and kernel theory. The presentation is focused on the no-bias-term' variant, accounts for several peculiarities specific to SVM regression and derives an active-set algorithm to solve the resulting large-scale quadratic programming problem. For linear systems, SVMs are combined with multi-stage methods for estimating output error and ARMAX models. Finally, two real-world processes serve as test cases to evaluate the SVMs' properties as nonlinear dynamic models. Copyright (c) 2013 John Wiley & Sons, Ltd.
Keyword:
support vector machine
system identification
convex optimization
quadratic programming
active-set method
kernel function
robust statistics
output error model
ARMAX model
application
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