Support vector regression for black-box system identification

Author

Gretton, Arthur ; Doucet, Arnaud ; Herbrich, Ralf ; Rayner, Peter J W ; Schölkopf, Bernhard

Author_Institution

Dept. of Eng., Cambridge Univ., UK

fYear

2001

fDate

6/23/1905 12:00:00 AM

Firstpage

341

Lastpage

344

Abstract

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 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

Keywords

identification; learning automata; manipulators; radial basis function networks; statistical analysis; black-box system identification; hydraulic robot arm; radial basis networks; statistical learning theory; support vector regression; Bayesian methods; Gradient methods; Machine learning algorithms; Maximum likelihood estimation; Monte Carlo methods; Neural networks; Robots; Signal processing; Statistical learning; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on

Print_ISBN

0-7803-7011-2

Type

conf

DOI

10.1109/SSP.2001.955292

Filename

955292

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1608544