Support vector regression for black-box system identification

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2001, Pages 341-344

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

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; POLYNOMIALS; RADIAL BASIS FUNCTION NETWORKS; REGRESSION ANALYSIS; VECTORS;

SUPPORT VECTOR REGRESSIONS (SVR);

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0035555821     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. The analysis of decomposition methods for support vector machines
2. Support vector machines for dynamic reconstruction of a chaotic system
3. Using support vector machines for time series prediction
4. New support vector algorithms
5. Nonlinear black-box modeling in system identification: A unified overview
6. Nonlinear black-box modeling in system identification: Mathematical foundations