Study of a new online Least Squares Support Vector Machine algorithm in gas prediction

Author

Zhao, Xiao-hu ; Zhao, Ke-ke

Author_Institution

Sch. of Commun. & Electron. Eng., China Univ. of Min. & Technol., Beijing

fYear

2008

fDate

13-16 July 2008

Firstpage

1026

Lastpage

1030

Abstract

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.

Keywords

coal; least squares approximations; mining industry; prediction theory; support vector machines; time series; coal mine gas prediction; online least squares support vector machine algorithm; time series prediction algorithm; Geology; Least squares methods; Machine learning; Prediction algorithms; Prediction methods; Predictive models; Production; Safety; Support vector machines; Time series analysis; LS-SVM; gas; online prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on

Conference_Location

Daejeon

ISSN

1935-4576

Print_ISBN

978-1-4244-2170-1

Electronic_ISBN

1935-4576

Type

conf

DOI

10.1109/INDIN.2008.4618253

Filename

4618253

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2751216