Online Least Squares Support Vector Machine Regression Based on Rectangular Window with Forgetting Factor Algorithm

被引：2

作者：

Guo Zhenkai ^{[1
]}

Song Zhaoqing ^{[2
]}

Mao Jianqin ^{[1
]}

机构：

[1] Beijing Univ Aeronaut & Astronaut, Res Div 7, Beijing 100083, Peoples R China

[2] Naval Aeronaut & Astronaut Univ, Dept Control Engn, Yantai 264001, Peoples R China

来源：

2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11 | 2008年

关键词：

Online Learning; OLS-SVMR; RWFF Algorithm; Chaotic Time Series;

D O I：

10.1109/CCDC.2008.4597540

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Considering of the problem that the online training for the standard least squares support vector machine (LS-SVM) is difficult, an new learning algorithm of online least squares support vector machine regression (OLS-SVMR) based rectangular window with forgetting factor (RWFF) algorithm is proposed, by combining the RWFF algorithm with support vector machine, the present and past window data are considered simultaneously. The proposed algorithm has less computation cost and high accuracy. The proposed method is proved, and then it is applied to forecast a chaotic time series. The effectiveness of the algorithm is demonstrated by the simulation results.

引用

页码：1363 / +

页数：2