Study of a New Online Least Squares Support Vector Machine Algorithm in Gas Prediction

被引：0

作者：

Zhao, Xiao-hu ^{[1
]}

Zhao, Ke-ke ^{[1
]}

机构：

[1] China Univ Min & Technol, Sch Commun & Elect Engn, Jiangsu 221008, Peoples R China

来源：

2008 6TH IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS, VOLS 1-3 | 2008年

关键词：

LS-SVM; gas; online prediction;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper studied on time series prediction, and proposes a new prediction algorithm of LS-SVM online learning against the shortcomings in the traditional online learning with Least Squares Support Vector Machine. This algorithm was researched and used in coal mine gas prediction and had proved effective, compared with the actual data and other relative algorithms.

引用

页码：988 / 992

页数：5

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