DocumentCode :
478204
Title :
Study of an Improved Online Least Squares Support Vector Machine Algorithm and Its Application in Gas Prediction
Author :
Zhao, Xiao-hu ; Zhao, Ke-ke
Author_Institution :
Sch. of Commun. & Electron. Eng., China Univ. of Min. & Technol., Xuzhou
Volume :
3
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
344
Lastpage :
348
Abstract :
The paper studied on gas prediction problem. According to traditional prediction methods on coal mine safety being offline without dynamic prediction function and the shortcomings in the traditional online learning with least squares support vector machine (LS-SVM), this paper gave an improved online prediction algorithm of LS-SVM. This algorithm was used in gas prediction of some coal mine. By comparing with the actual data and other relative algorithms, the paper proved effect of the algorithm.
Keywords :
coal; least squares approximations; mining; safety; support vector machines; coal mine safety; gas prediction; online least squares support vector machine algorithm; Equations; Geology; Least squares methods; Neural networks; Paper technology; Prediction methods; Predictive models; Quadratic programming; Safety; Support vector machines; LS-SVM; gas; online prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.881
Filename :
4667158
Link To Document :
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