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Support vector regression for black-box system identification

Abstract:
In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. These methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydraulic robot arm, and show that SVR improves on previously published results.
Publication status:
Published

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Host title:
2001 IEEE WORKSHOP ON STATISTICAL SIGNAL PROCESSING PROCEEDINGS
Pages:
341-344
Publication date:
2001-01-01
ISBN:
0780370112
Pubs id:
pubs:190607
UUID:
uuid:6f306d7e-9927-4fca-b2e9-c31c71d72f05
Local pid:
pubs:190607
Source identifiers:
190607
Deposit date:
2012-12-19

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