基于灰色神经网络预测青东矿10煤层底板破坏深度  

Floor 10 Damage Depth of Green East Coal Mine Based on Grey Neural Network

作　　者：陈从磊[1] 姚多喜[1] 赵魁[1] 杨清[1] 程刚[1] 

机构地区：[1]安徽理工大学地球与环境学院,安徽淮南232001

出　　处：《煤炭技术》2012年第11期49-51,共3页Coal Technology

摘　　要：传统的预测底板破坏深度方法主要有力学及数理统计、定性比较分析等,然而这些方法的预测值往往达不到预期的效果或与实际值差距较大。通过对比检验样本的预测误差可得出基于灰色理论的神经网络预测模型的精度高于BP神经网络的预测结果。故此次选用灰色理论与神经网络相结合的方法建立模型预测青东矿104采区10煤底板破坏深度,预测结果为16.86m。The traditional forecasting the bottom water damage depth method mainly powerful learning and mathematical statistics, the qualitative comparison analysis and so on. But these methods, the predicted often hit the desired effect or bigger difference with the actual value. Through the contrast test sample can be concluded that the prediction error based on the grey theory of neural network forecast the precision of the model is higher than the BP neural network of prediction results. Therefore, this choice of gray theory and method of combining neural network model predicted the East Mine 104 Green coal mining area 10 floor failure depth and the results of predictions is16.86 meters.

关 键 词：灰色理论 BP神经网络 破坏深度 

分 类 号：TD30[矿业工程—矿井建设]