Mining Working Face Time Series Short-term Gas Prediction Based on LS-SVM

被引：0
|
作者：
Qiao Meiying [1 ,2 ]
Ma Xiaoping [1 ]
机构：
[1] China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221008, Peoples R China
[2] Henan Polytech Univ, Sch Elect Engn & Automat, Jiaozuo 454000, Peoples R China
来源：
2010 INTERNATIONAL CONFERENCE ON MINE HAZARDS PREVENTION AND CONTROL | 2010年 / 12卷
基金：
中国国家自然科学基金;
关键词：
LS-SVM; Time series; Short-term gas prediction;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
At present, one of development direction of mine gas prediction is the statistical learning method. In this paper author firstly introduces the character of SVM, and on this basis give the basic principle of LS-SVM, and at the same time establish LS-SVM regression model. Secondly, the data of time series gas concentration are standardized in the range of [-1, 1], subsequently these data are reconstructed and used for training data and test data. Finally, in the MATLAB7.1 environment, this prediction model is achieved by algorithm procedure. The working face gas outburst data of the 10th coal mine in Hebi is used to train and test this model. According to two examples simulation result shows that this model has well the short-term working face gas predict effects.
引用
收藏
页码：343 / +
页数：3
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