基于PLS-BP神经网络组合模型的回采工作面瓦斯涌出量预测  

Based on PLS associated with BP neural network for different- source gas emission prediction model of working face

作　　者：高保彬[1] 潘家宇[1] 

机构地区：[1]河南理工大学河南省瓦斯地质与瓦斯治理重点实验室,安全科学与工程学院,河南焦作454000

出　　处：《湖南科技大学学报（自然科学版）》2015年第4期14-20,共7页Journal of Hunan University of Science And Technology：Natural Science Edition

基　　金：国家自然科学基金资助项目(u1304502);河南省瓦斯地质与瓦斯治理重点实验室--省部共建国家重点实验室培育基地项目(WS2012B06);教育部博士点基金(20134116120001);河南理工大学博士基金(B2011-011)

摘　　要：提出PLS-BP神经网络组合模型,预测回采工作面瓦斯涌出量.利用分源预测法划分回采工作面瓦斯涌出来源,根据瓦斯涌出来源受不同因素的影响,运用偏最小二乘法(PLS),通过交叉有效性分析,确定提取主成分个数,将主成分作为神经网络输入层建立关联模型.研究证明,本方法不仅避免了各种不相关因素之间的干扰,解决各因素之间多重相关问题,降低变量维数,而且可以结合BP神经网络的非线性映射能力和适应学习能力等优点,提高预测稳定性和精度.The prediction of gas emission from working face is very important to the mine safety production.Different- source prediction was used to divide gas emission,according to the gas emission is influenced by different factors,partial least square method（ PLS） was used that of cross validity analysis,to determine the principal component number,the principal component was regarded as neural network input layer and correlation model was set up. The result showed that this method not only avoids the interference between the various related factors,solves the problem of multiple correlation among various factors,reduces the variable dimension,but also by nonlinear mapping capability of BP neural network and adaptive learning ability,improve the prediction accuracy and stability.

关 键 词：偏最小二乘法 分源预测法 交叉有效性分析 BP神经网络模型 瓦斯涌出量 

分 类 号：TD712.3[矿业工程—矿井通风与安全]