On System Identification Based on Online Least Squares Support Vector Machine

DOI

10.2991/iske.2007.184How to use a DOI?

Keywords

System identification, Online learning, Least squares support vector machine

Abstract

System identification is a fundamental topic of control theory, and LS-SVM has been applied to system identification. An online training algorithm of LS-SVM for system identication is presented, which is suitable for the data set supplied in sequence rather than in batch. The online algorithm avoids computing large-scale matrix inverse when the number of support vectors changes, thus the computation time is reduced. In order to validate the performance of the online algorithm, the system identification experiments are considered. The simulation results show that the online training algorithm is suitable for the online system identification.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bin Liu
AU  - Zhiping Wang
PY  - 2007/10
DA  - 2007/10
TI  - On System Identification Based on Online Least Squares Support Vector Machine
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 1084
EP  - 1087
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.184
DO  - 10.2991/iske.2007.184
ID  - Liu2007/10
ER  -