Coal Face Gas Emission Prediction Based on Support Vector Machine

被引：2
|
作者：
Ning Yuncai [1 ]
Chen Xiang [1 ]
机构：
[1] China Univ Min & Technol, Inst Management, Beijing, Peoples R China
来源：
2009 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTATIONAL INTELLIGENCE, VOL I, PROCEEDINGS | 2009年
关键词：
gas emission quantity; support vector machine; index system; prediction;
D O I：
10.1109/AICI.2009.217
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Mine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex non-linear relationship. The paper built the work face gas emission prediction support vector machine (SVM) model. Based on data statistic of a mine work face gas emission, the paper used the model to predict gas emission. The result was accurate, which prove the model's prediction for face gas is viable and effective.
引用
收藏
页码：19 / 22
页数：4
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