Title :
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
fDate :
6/23/1905 12:00:00 AM
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;
Conference_Titel :
Statistical Signal Processing, 2001. Proceedings of the 11th IEEE Signal Processing Workshop on
Print_ISBN :
0-7803-7011-2
DOI :
10.1109/SSP.2001.955292
