Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Conference Paper

Support Vector Regression for Black-Box System Identification

There are no MPG-Authors in the publication available
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

Gretton, A., Doucet, A., Herbrich, R., Rayner, P., & Schölkopf, B. (2001). Support Vector Regression for Black-Box System Identification. In 11th IEEE Workshop on Statistical Signal Processing (pp. 341-344). Piscataway, NJ, USA: IEEE.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E37C-8
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.