山东煤炭科技
山東煤炭科技
산동매탄과기
Shandong Coal Science and Technology
2013年
4期
209-210,212
,共3页
瓦斯含量%神经网络%预测
瓦斯含量%神經網絡%預測
와사함량%신경망락%예측
gas content neural%network predict
该文研究了BP神经网络建立瓦斯含量预测模型的数学原理及数值算法，收集了平顶山十矿己15-16煤层地勘期间及生产期间的瓦斯含量实测资料，获得了9个可靠点，选取埋藏深度、煤层厚度和煤层厚度作为输入元，建立了基于BP神经网络的瓦斯含量预测模型。根据计算和评价结果，模型精度能够满足工程精度的要求，说明用BP神经网络来预测平煤十矿己15-16煤层瓦斯瓦斯含量是可行的。
該文研究瞭BP神經網絡建立瓦斯含量預測模型的數學原理及數值算法，收集瞭平頂山十礦己15-16煤層地勘期間及生產期間的瓦斯含量實測資料，穫得瞭9箇可靠點，選取埋藏深度、煤層厚度和煤層厚度作為輸入元，建立瞭基于BP神經網絡的瓦斯含量預測模型。根據計算和評價結果，模型精度能夠滿足工程精度的要求，說明用BP神經網絡來預測平煤十礦己15-16煤層瓦斯瓦斯含量是可行的。
해문연구료BP신경망락건립와사함량예측모형적수학원리급수치산법，수집료평정산십광기15-16매층지감기간급생산기간적와사함량실측자료，획득료9개가고점，선취매장심도、매층후도화매층후도작위수입원，건립료기우BP신경망락적와사함량예측모형。근거계산화평개결과，모형정도능구만족공정정도적요구，설명용BP신경망락래예측평매십광기15-16매층와사와사함량시가행적。
The mathematic principles and numerical algorithm of BP neural network for gas contents were firstly studied ,Then,the actual measurement data of gas contents during geological prospecting and mining of PingdingshanNO.10mine JI15 -16 coal seam were collected,and 9 reliable dots were gained.By selecting 2 factors including depth、coal seam thicknessy as the input element ,and the multivariate forecast models of gas contents based on BP neural network were respectively constructed.According to the calculation and evaluation of results,accuracy of the model to meet the requirements of en-gineering precision,indicated that BP neural network to predict gas content of PingdingshanNO.10mine JI15 -16 coal seam is feasible.
