On system identification based on online least squares support vector machine

被引：0
|
作者：
Liu, Bin [1 ]
Wang, Zhiping [1 ]
机构：
[1] Shaanxi Univ Sci & Technol, Dept Comp, Xianyang 712081, Peoples R China
来源：
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007) | 2007年
关键词：
system identification; online learning; least squares support vector machine;
D O I：
10.2991/iske.2007.184
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 identication 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.
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页数：1
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