工矿自动化2012，Vol.38Issue(10)：42-46,5.
基于蚁群—模糊聚类算法的井下工作面瓦斯突出预测
Gas Outburst Prediction of Underground Working Face Based on ACA-FCM Algorithm
周天沛 1孙伟2
作者信息
- 1. 徐州工业职业技术学院机电工程学院,江苏徐州221140
- 2. 中国矿业大学信电学院,江苏徐州 221008
- 折叠
摘要
Abstract
In view of problems of great limitation in actual application and bad precision of prediction
result of current gas outburst prediction method, the paper proposed a gas outburst prediction method based on ACA-FAM algorithm. It analyzed basic principle and implementation steps of ACA-FAM algorithm. Taking data of gas outburst of underground working face of a Coal Mine in a certain period as example, it used ACA-FAM algorithm to make mining analysis for the data to find relations between gas outburst and influencing factors such as buried depth, coal seam thickness, gas content, daily advance, coal seam interval and daily output. The test result shows that prediction result of the method is uniform with actual monitoring record and the method has higher classified prediction performance.关键词
井下工作面/瓦斯突出预测/蚁群算法/模糊聚类算法Key words
underground working face/gas outburst prediction/ant colony algorithm/fuzzy clustering analysis algorithm分类
矿业与冶金引用本文复制引用
周天沛,孙伟..基于蚁群—模糊聚类算法的井下工作面瓦斯突出预测[J].工矿自动化,2012,38(10):42-46,5.
