BP和RBF在断层防水煤柱留设宽度预测中的应用  

Prediction of remained pillar against water-inrush from fault by using BP and RBF neural networks

作　　者：徐东晶[1,2] 施龙青[1,2] 邱梅[1,2] 景行[1,2] 孙祺[1,2] 

机构地区：[1]山东省沉积成矿作用与沉积矿产重点实验室,山东青岛266590 [2]山东科技大学地质科学与工程学院,山东青岛266590

出　　处：《煤田地质与勘探》2013年第4期66-69,共4页Coal Geology & Exploration

基　　金：国家自然科学基金项目(41072212);山东省自然科学基金重点项目(ZR2011EEZ002);山东科技大学科研创新团队支持计划项目(2012KYTD101);山东科技大学研究生科技创新基金项目(YC130207)

摘　　要：在总结全国各典型煤矿断层防水煤柱相关资料的基础上,以水头压力、煤层厚度、安全系数、煤的抗张强度为主要影响因子,选择有代表性的样本数据,通过Matlab软件构建了BP和RBF神经网络模型,对各煤矿断层防水煤柱的留设宽度进行了预测,并与规程经验公式计算的结果进行了对比。结果显示,在煤矿断层防水煤柱留设宽度预测中,RBF神经网络比BP神经网络的训练速度更快,效率更高,具有更加广阔的应用前景。On the basis of summing up the relevant information of the safety pillar against water-inrush from fault in typical coal mines throughout the country,taking head pressure,coal seam thickness,safe coefficient,coal tensile strength as the main influence factors,choosing the representative sample data,we built the BP and RBF neural network model through the Matlab software,with the model we forecasted the designed width of the water-proof coal pillar in each coal mine.Compared with the calculated results of procedure empirical formula,on the research of the design about water-proof pillar from coalfield,we found that it was faster for RBF neural networks in training speed than the BP neural networks,furthermore,RBF is much more efficient than BP and can get broader application prospects.

关 键 词：BP神经网络 RBF神经网络 煤柱宽度预测 断层构造 MATLAB软件 

分 类 号：TD163[矿业工程—矿山地质测量]