Abstract
This paper develops a new approach based on Least Squares Support Vector Machines (LS-SVMs) for parameter estimation of time invariant as well as time varying dynamical SISO systems. Closed-form approximate models for the state and its derivative are first derived from the observed data by means of LS-SVMs. The time-derivative information is then substituted into the system of ODEs, converting the parameter estimation problem into an algebraic optimization problem. In the case of time invariant systems one can use least-squares to solve the obtained system of algebraic equations. The estimation of time-varying coefficients in SISO models, is obtained by assuming an LS-SVM model for it.
| Original language | English |
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| Title of host publication | SYSID 2012 - 16th IFAC Symposium on System Identification, Final Program |
| Publisher | IFAC Secretariat |
| Pages | 1300-1305 |
| Number of pages | 6 |
| Edition | PART 1 |
| ISBN (Print) | 9783902823069 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | Universite Libre de Bruxelles - Bruxelles, Belgium Duration: 11 Jul 2012 → 13 Jul 2012 |
Publication series
| Name | IFAC Proceedings Volumes (IFAC-PapersOnline) |
|---|---|
| Number | PART 1 |
| Volume | 16 |
| ISSN (Print) | 1474-6670 |
Conference
| Conference | Universite Libre de Bruxelles |
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| Country/Territory | Belgium |
| City | Bruxelles |
| Period | 11/07/12 → 13/07/12 |
Bibliographical note
Funding Information:⋆ This work was supported by Research Council KUL: GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering(OPTEC), IOF-SCORES4CHEM, several PhD/postdoc & fellow grants;Flemish Government:FWO: PhD/postdoc grants, projects: G0226.06 (cooperative systems and optimization), G0321.06 (Tensors), G.0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08 (Glycemia2), G.0588.09 (Brain-machine) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC) IWT: PhD Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O&O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelli-CIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; Other:Helmholtz: viCERP, ACCM, Bauknecht, Hoerbiger. Johan Suykens is a professor at the KU Leuven, Belgium.
Funding
⋆ This work was supported by Research Council KUL: GOA/11/05 Ambiorics, GOA/10/09 MaNet, CoE EF/05/006 Optimization in Engineering(OPTEC), IOF-SCORES4CHEM, several PhD/postdoc & fellow grants;Flemish Government:FWO: PhD/postdoc grants, projects: G0226.06 (cooperative systems and optimization), G0321.06 (Tensors), G.0302.07 (SVM/Kernel), G.0320.08 (convex MPC), G.0558.08 (Robust MHE), G.0557.08 (Glycemia2), G.0588.09 (Brain-machine) research communities (WOG: ICCoS, ANMMM, MLDM); G.0377.09 (Mechatronics MPC) IWT: PhD Grants, Eureka-Flite+, SBO LeCoPro, SBO Climaqs, SBO POM, O&O-Dsquare; Belgian Federal Science Policy Office: IUAP P6/04 (DYSCO, Dynamical systems, control and optimization, 2007-2011); EU: ERNSI; FP7-HD-MPC (INFSO-ICT-223854), COST intelli-CIS, FP7-EMBOCON (ICT-248940); Contract Research: AMINAL; Other:Helmholtz: viCERP, ACCM, Bauknecht, Hoerbiger. Johan Suykens is a professor at the KU Leuven, Belgium.
Keywords
- Deterministic dynamic models
- Least squares support vector machines
- Parameter estimation
- Time-varying parameters