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Support Vector Regression for Black-Box System Identification

2001

Conference Paper

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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.

Author(s): Gretton, A. and Doucet, A. and Herbrich, R. and Rayner, P. and Schölkopf, B.
Journal: 11th IEEE Workshop on Statistical Signal Processing
Pages: 341-344
Year: 2001
Day: 0
Publisher: IEEE Signal Processing Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 11th IEEE Workshop on Statistical Signal Processing

Address: Piscataway, NY, USA
Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PostScript

BibTex

@inproceedings{1851,
  title = {Support Vector Regression for Black-Box System Identification},
  author = {Gretton, A. and Doucet, A. and Herbrich, R. and Rayner, P. and Sch{\"o}lkopf, B.},
  journal = {11th IEEE Workshop on Statistical Signal Processing},
  pages = {341-344},
  publisher = {IEEE Signal Processing Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NY, USA},
  year = {2001}
}