摘要
为了提高煤层底板透水预测的效率和准确性,将主成分分析(PCA)与神经网络(BP)相结合,对煤层底板透水进行预测。根据搜集到的煤层底板透水的影响因素及其相关数据。通过收集不同矿井透水资料,综合考虑多种影响煤层底板透水的因素,利用主成分分析(PCA)法提取影响因素的主成分,建立PCA-BP煤层底板透水预测模型。选取典型的矿井透水样本进行工程实践验证,结果表明本预测模型符合实际情况。
In order to improve the efficiency and accuracy of the water permeation prediction of coal seam floor,the principal component analysis（ PCA） is combined with the neural network（ BP） to predict the permeability of coal seam floor.According to the collected influence factors and related data of the coal seam floor.By collecting different data of mine permeability,considering various factors that affect coal floor waterirruption,using principal component analysis（ PCA） method to extract the main component of influencing factors,PCA-BP coal floor water prediction model is established.The results show that the prediction model conforms to the actual situation.
作者
王如猛
王少强
WANG Ru-Meng WANG Shao-Qiang(School of Mining and Safiy Engineering,Shandong University of Science and Technology, Qingdao, 266590, China Key Laboratory of Mine Hazard Prevention and Control, Ministry of Education, Shandong University of Science and Technology, Qingdao , 266590, China)
出处
《华北科技学院学报》
2017年第4期29-33,共5页
Journal of North China Institute of Science and Technology
关键词
PCA-BP
底板突水
主成分分析
神经网络
影响因素
工程实践
PCA-BP
floor water irruption
principal component analysis
neural networks
influencing factors
engineering practice
