期刊文献+

基于支持向量机的采空区遗煤自燃预测分析 被引量:9

Analysis on Prediction of Residual Coal Spontaneous Combustion in Goaf Based on Support Vector Machine
下载PDF
导出
摘要 在分析回采工作面煤层自燃基础上,用支持向量机(SVM)预测技术对采空区遗煤自燃进行预测分析。并以忻州窑矿8914综放工作面采空区遗煤自燃为实例,对其不同时期采空区危险性进行预测分析,验证了该方法的可行性。打破传统经验分析法,实现了煤矿安全分析的智能化,对建立本质安全型矿井具有利用价值。 Based on the analysis on the seam spontaneous combustion in the coal mining face, the prediction technology with the support vector machine was applied to predict and analyze the spontaneous combustion danger of the residual coal in the goaf. Taking the residual coal spontaneous combustion in the goal of the No. 8914 fully mechanized top coal caving mining face in Xinzhouyao Mine as a case, the danger of the goaf during the different periods was predicted and analyzed, the feasibility of the method was approved. To break the con- ventional experience analysis method, the mine safety intelligent analysis could be conducted and would have the applied value to establish the intrinsic safety mine.
出处 《煤炭科学技术》 CAS 北大核心 2010年第2期50-54,共5页 Coal Science and Technology
基金 "十一五"国家科技支撑计划资助项目(2006BAK03B02)
关键词 采空区 遗煤 自燃 支持向量机 goaf residual coal spontaneous combustion support vector machine
  • 相关文献

参考文献9

二级参考文献36

  • 1徐精彩,葛岭梅,贺敦良.煤炭低温自燃过程的研究[J].煤炭工程师,1989(5):7-13. 被引量:69
  • 2南存全,冯夏庭.凹形圆弧断裂构造的简化力学模型及其解析分析[J].岩石力学与工程学报,2004,23(23):3984-3989. 被引量:10
  • 3煤炭工业部.防治煤与瓦斯突出细则[M].北京:煤炭工业出版社,1995..
  • 4Lama R D, Bodziony J. Management of outburst in underground coal mines[J]. International Journal of Coal Geology, 1998, 35(1): 83 -115.
  • 5Feng Xiating, Katsuyama, Wang Yongjia, et al. A new direction intelligent rock mechanics and rock engineering[J]. International Journal of Rock Mechanics and Mining Science, 1997, 34(1): 135 -141.
  • 6Christopher J C, Burges. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998,2(2): 121-167.
  • 7Amari S, Wu S. Improving support vector machine classifier by modifying kernel function[J]. Neural Networks, 1999, 12(6): 783 -789.
  • 8Bartlett P L, Taylor S J. Generalization performance on support vector machines and other pattern classifiers [A]. In: Advances in Kernel Methods-Support Vector Learning[C]. Cambridge: MIT Press, 1999.236 - 248.
  • 9S Raudys. How good are support vector machines[J]. Neural Networks, 2000, 13(1): 17-19.
  • 10李路 袁震东.用小波塔式分解进行心电图分割[A]..第四届全球智能控制与自动化大会论文集[C].上海:华东理工大学出版社,2002..

共引文献112

同被引文献128

引证文献9

二级引证文献136

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部