中北大学学报（自然科学版）2018，Vol.39Issue(3)：303-309,7.DOI:10.3969/j.issn.1673-3193.2018.03.010
基于改进型主元分析和SVR的煤矿瓦斯涌出量预测
Prediction of Gas Emission in Coal Mine Based on Improved PCA and SVR
张文东 1胡彧1
作者信息
- 1. 太原理工大学 测控技术研究所,山西 太原030024
- 折叠
摘要
Abstract
Due to the shortcomings of traditional principal component analysis and the characteristics of the samples,and combined with the influence factors of coal seam gas emission,the prediction model of coal seam gas emission was established,which based on the improved principal component analysis of weight and support vector regression machine.Firstly,it used the improved principal component analy-sis method to analysis the influence factors,obtain the principal component.secondly,it used principal components as input variables and gas emission quantity as output variables,then established the predic-tion model based on support vector regression machine.Experiment results show that the model can be used to eliminate the correlation between input variables and reduce the number of input variables,it can effectively solve the problems of small sample and complex model,it also improves the accuracy of pre-diction.关键词
煤层瓦斯/涌出预测/改进主元分析/支持向量回归机Key words
coal seam gas/emission prediction/improved PCA/SVR分类
信息技术与安全科学引用本文复制引用
张文东,胡彧..基于改进型主元分析和SVR的煤矿瓦斯涌出量预测[J].中北大学学报（自然科学版）,2018,39(3):303-309,7.
