期刊文献+

综掘工作面瓦斯涌出量的支持向量机预测模型 被引量:7

Prediction model of support vector machine for gas emission quantity of fully mechanized roadway driving face
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摘要 通过对支持向量机回归原理的分析,设计出预测综掘工作面瓦斯涌出量的支持向量机模型。然后,在所设计模型的基础上,采用Matlab语言,实现了该模型。运用训练样本对模型进行训练和实际数据样本进行仿真,得到了很好的预测效果。从预测结果可以看出,达到了工程实际能够接受的预测精度,说明该模型能够用于矿井的综掘工作面瓦斯涌出量预测。 Based on the analysis on the regression principle of the support vector machine, a support vector machine model was established to predict the gas emission of the fully mechanized roadway driving workface. Base on the model designed, MATLAB software was applied to the model. With the training text, the model was trained and the simulation test was conducted with the actual data text. Good results of the prediction were obtained. The prediction results showed that the actual engineering could accept the predicted accuracy and the model could be applied to the prediction of the gas emission for the fully mechanized roadway driving face in the mine.
出处 《煤炭工程》 北大核心 2009年第2期75-77,共3页 Coal Engineering
关键词 综掘工作面 瓦斯涌出量 支持向量机 预测 fully - mechanized coal driving workface gas emission support vector machine prediction
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参考文献12

  • 1撒占友 何学秋 王恩元.基于自适应神经网络的采掘工作面瓦斯涌出量预测.煤炭学报,2001,26(0):96-99.
  • 2杨智懿,熊亚选,张乾林.工作面瓦斯涌出量的神经网络模型预测研究[J].煤炭工程,2004,36(10):73-75. 被引量:25
  • 3刘玉静,肖广智.用人工神经网络模型预测采煤工作面的瓦斯涌出量[J].煤矿安全,2003,34(1):11-13. 被引量:10
  • 4罗公亮.从神经网络到支撑矢量机(上)[J].冶金自动化,2001,25(5):1-5. 被引量:20
  • 5Vapnik V N. The Nature of Statistical Learning Theory [ M ]. New York Spinger- Verleg, 1995.
  • 6Vapnik V N. Statistical Learning Theory [ M ]. New York: Jone Wileg, 1998.
  • 7Gunn S. Support Vector Machine for Classification and Regression [ R]. ISIS Report Image Speck & Intelligent System Group, University of southamptm, 1998.
  • 8边肇祺 张学工 等.模式识别[M].北京:清华大学出版社,2001..
  • 9Keerchi, Lin C J. Asymptotic behaviors of support vector machines with Gaussian kernel [ J ]. Neural Computation, 2003, (15) : 1667-1689.
  • 10Lin H T, Lin C J. A study on sigmoid kernels for SVM and the training of non - PSD kernels by SMO - type methods [ EB ], 2003.

二级参考文献12

  • 1陈文敏,姜宁.煤灰成分和煤灰熔融性的关系[J].洁净煤技术,1996,2(2):34-37. 被引量:49
  • 2[3]杨建刚.人工神经网络使用教程[M].浙江大学出版社,2001.
  • 3NelloCristianini JohnShawe-Taylor 李国正 王猛 曾华军译.支持向量机导论[M].北京:电子工业出版社,2004..
  • 4Vapnik.统计学习理论[M].张学工,译.北京:电子工业出版社,2004.
  • 5煤炭工业部.全国煤质资料汇编[M].[s.l]:[s.n.],1981.
  • 6Yin Chungen,Luo Zhongyang,Ni Mingjiang,et al.Predicting coal ash fusion temperature with a back-propagation neural network model[J].Fuel,1998,77(15):1 777~1 782.
  • 7Kalogirou S A.Artificial intelligence for the modeling and control of combustion processes;A review[J].Progress in Energy and Combustion Seience,2003,29:515~566.
  • 8Lin H T,Lin C J.A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods[EB].2003.
  • 9Z 米凯利维茨.演化程序[M].周家驹,何险峰,译.北京:科学出版社,2000.
  • 10Keerthi,Lin C J.Asymptotic behaviors of support vector machines with Gaussian kernel[J].Neural Computation,2003,15,1 667~1 689.

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