辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(12)：1246-1250,5.DOI:10.11956/j.issn.1008-0562.2017.12.003
基于PCA-RBF网络的煤与瓦斯突出强度预测
Prediction of coal and gas outburst intensity based on PCA-RBF network
摘要
Abstract
In order to increase the accuracy of coal and gas outburst intensity prediction,this paper proposed that principal component analysis (PCA) can be adopted to reduce the correlation between variables and radial basis function network which was combined to predict coal and gas outburst intensity.Taking a coal mine as the object of research,the principal components of influencing factors of coal and gas outburst intensity were extracted.And three principal components with contribution rate of accumulated variance which was more than 85％ were selected and used to replace the six original factors.The principal components were regarded as the input parameters of radial basis function network and PCA-RBF network prediction model was built.The results showed that the average relative error of PCA-RBF network prediction model was 5.55％,and it was accord with the requirements of coal and gas outbursts prediction.关键词
煤与瓦斯突出强度/主成分分析/径向基(RBF)网络/突出强度预测Key words
coal and gas outburst intensity/principal component analysis/radial basis function (RBF) network/outburst intensity prediction分类
矿业与冶金引用本文复制引用
周西华,徐丽娜,董强,郭晓阳..基于PCA-RBF网络的煤与瓦斯突出强度预测[J].辽宁工程技术大学学报（自然科学版）,2017,36(12):1246-1250,5.基金项目
国家自然科学基金(51274115,51274113) （51274115,51274113）
辽宁省教育厅基金(L2012122) （L2012122）
