Premium
System identification techniques based on support vector machines without bias term
Author(s) -
Vogt Michael
Publication year - 2013
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2404
Subject(s) - support vector machine , computer science , machine learning , artificial intelligence , statistical learning theory , term (time) , kernel (algebra) , relevance vector machine , nonlinear system , merge (version control) , data mining , mathematics , physics , quantum mechanics , combinatorics , information retrieval
SUMMARY 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 © 2013 John Wiley & Sons, Ltd.