|本期目录/Table of Contents|

[1]刘程程,杨力.PCA-SVR在煤层瓦斯含量预测中的应用[J].中国安全生产科学技术,2012,8(7):78.
　LIU Cheng cheng,YANG Li.Application of PCASVR on gas content predicting in coal seam[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2012,8(7):78.
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PCA-SVR在煤层瓦斯含量预测中的应用
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
8
期数:
2012年7期
页码:
78
栏目:
出版日期:
2012-07-31

文章信息/Info

Title:
Application of PCASVR on gas content predicting in coal seam
作者:
刘程程; 杨力
（安徽理工大学 经济与管理学院，淮南 232001）
Author(s):
LIU Chengcheng;  YANG Li
(College of Economic and Management, Anhui University of Science and Technology, Huainan 232001, China)
关键词:
主成分分析; 支持向量回归机; 预测; 煤层瓦斯含量
Keywords:
principal component analysis (PAC);  support vector regression machine (SVR);  prediction;  gas content in coal seam
分类号:
X936
DOI:
-
文献标志码:
A
摘要:
针对煤层瓦斯含量与其影响因素之间存在着复杂的非线性关系，建立了基于主成分分析和支持向量回归机的煤层瓦斯含量预测模型。该模型有效地解决了小样本、非线性预测的问题，并发挥了主成分分析法消除输入变量间相关性的优点，减少了输入变量个数，提高了预测精度和收敛速度。通过实证分析，该模型的预测精度高，能够直接用于煤矿现场预测煤层瓦斯含量。
Abstract:
In view of existing complicated nonlinear relation between gas content in coal seam and its influence factors, a prediction model of gas content was constructed based on principal component analysis and support vector regression machine. The model can effectively solve the problems of small sample and nonlinear prediction; and makes use of principal components analysis to eliminate correlation between input variables, which reduces numbers of input variables to improve prediction precision and convergence rate. Through the empirical analysis, the prediction precision of this model was higher, which can be directly applied to predicting gas content in coal seam on the spot.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金项目(编号：71071003)；教育部人文社会科学研究项目(编号：09YJC630004)
更新日期/Last Update: 2012-08-06