摘要
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。
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.
出处
《山东煤炭科技》
2013年第4期209-210,212,共3页
Shandong Coal Science and Technology
关键词
瓦斯含量
神经网络
预测
gas content
neural network
predict
