System identification based on least squares support vector machine

被引：0
|
作者：
Zhu, Zhiyu [1 ]
Zhang, Bin [1 ]
Liu, Weiting [1 ]
机构：
[1] Jiangsu Univ Sci & Tehcnol, Zhenjiang 212003, Jiangsu, Peoples R China
来源：
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS | 2006年 / 13卷
关键词：
D O I：
暂无
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Support vector machine(SVM) is applied to resolve nonlinear system identification in this paper. Based on insensitive loss function, Least Squares SVM(LS-SVM) is discussed who can get optimal solution through resolving a group of linear equations with Least Squares algorithm, instead of quadric programming in the standard SVM. As a result the algorithm complexity is greatly decreased. In simulation experiment, it is discuss how to select parameters of SVM and kernel function. Simulation results indicate LS-SVM has stronger identification ability and faster convergence speed, compared with the standard SVM.
引用
收藏
页码：589 / 592
页数：4
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