Mining Working Face Time Series Short-term Gas Prediction Based on LS-SVM
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.
LS-SVM Time series Short-term gas prediction
QIAO Meiying MA Xiaoping
School of Information and Electrical Engineering, China University Mining and Technology, Xuzhou, 22 School of Information and Electrical Engineering, China University Mining and Technology, Xuzhou, 22
国际会议
2010 International Conference on Mine Hazards Prevention and Control(第二届矿山灾害预防与控制国际学术会议 ICMHPC)
青岛
英文
343-348
2010-10-15（万方平台首次上网日期，不代表论文的发表时间）