基于PCA-RVM的矿山岩石爆破粒径预测模型  

PCA-RVM based model for particle size prediction of blasted mine rock

作　　者：张研[1,2] 吴哲康 ZHANG Yan;WU Zhe-kang(Guangxi Key Laboratory of Geomechanics and Geotechnical Engineering,Guilin University of Technology,Guilin 541004,China;School of Civil and Architectural Engineering,Guilin University of Technology,Guilin 541004,China)

机构地区：[1]桂林理工大学广西岩土力学与工程重点实验室,广西桂林541004 [2]桂林理工大学土木与建筑工程学院,广西桂林541004

出　　处：《沈阳工业大学学报》2023年第2期229-234,共6页Journal of Shenyang University of Technology

基　　金：国家自然科学基金项目(52068016);广西自然科学基金项目(2020GXNSFAA297118,2020GXNSFAA159125);广西岩土力学与工程重点实验室项目(桂科能20-Y-XT-01)。

摘　　要：为解决露天矿山爆破开采过程中岩石爆破粒径大小难以获取的问题,提出一种基于主成分分析法(PCA)及相关向量机(RVM)相结合的矿山岩石爆破粒径预测模型.该模型利用PCA对样本数据进行降维处理,选取出4个相互独立的主成分变量,并借助RVM构建主成分与爆破粒径之间的非线性映射关系,从而建立预测模型.将该模型应用于工程实例,并与BP神经网络和LM双隐含层模型进行对比.结果表明,在相同学习样本下,PCA-RVM模型预测结果与实际值更加接近,在平均相对误差和均方差上远小于另两种模型.In order to resolve the difficulties in obtaining the particle size of blasted rock in the process of blasting in open pit mines, a predication model based on principal component analysis(PCA) and relevance vector machine(RVM) for the particle size of blasted rock in mine was proposed. The model reduced the dimensionality of sample data by using PCA, selected four independent principal component variables and constructed a non-linear mapping relationship between the principal components and the blasting size with the help of RVM, and the prediction model was formulated consequently. This model was applied to an engineering case, in comparison with BP neural network and LM double hidden layer model. The results indicate that the prediction results of PCA-RVM model are closer to the actual values with much smaller average relative error and mean squared error than those of other two models, under the condition of identical learning samples.

关 键 词：露天矿山 主成分分析 相关向量机 爆破 岩石粒径 降维处理 非线性映射 预测模型 

分 类 号：TD235[矿业工程—矿井建设]