| 基于PCA-SVR的煤层底板突水量预测 |
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| 引用本文： | 刘北战, 梁冰. 基于PCA-SVR的煤层底板突水量预测[J]. 煤田地质与勘探, 2011, 39(1): 28-30,35. DOI: 10.3969/j.issn.1001-1986.2011.01.007 |
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| 作者姓名： | 刘北战 梁冰 |
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| 作者单位： | 1. 辽宁工程技术大学理学院, 辽宁 阜新 123000; |
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| 摘 要： | 提出了一种基于主成分分析支持向量机回归(PCA-SVR)的煤层底板突水预测方法，用主成分分析来解决输入变量的选择问题。主成分以较少的维数包含了高维变量所携带的大部分信息，这不仅避免了过多的输入导致训练速度慢，同时也保证了预测准确度。实例表明，所提方法可有效消除众多影响因素间的相关性，减少输入变量个数，提高预测效率和精度。
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| 关 键 词： | 主成分分析 支持向量机 煤层底板 突水 预测 |
| 收稿时间： | 2010-05-02 |
Prediction of seamfloor water inrush based on combining principal component analysis and support vector regression |
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| LIU Beizhan, LIANG Bing. Prediction of seamfloor water inrush based on combining principal component analysis and support vector regression[J]. COAL GEOLOGY & EXPLORATION, 2011, 39(1): 28-30,35. DOI: 10.3969/j.issn.1001-1986.2011.01.007 |
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| Authors: | LIU Beizhan LIANG Bing |
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| Affiliation: | 1. College of Science, Liaoning Technical University, Fuxin 123000, China;2. College of Mechanics and Engineering, Liaoning Technical University, Fuxin 123000, China |
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| Abstract: | This paper proposed a prediction method that is based on combing principal component analysis and support vector regression.Principal component analysis was used to select input variables.The prediction model considers all-sided influencing factors and avoids the low precision and slow training induced by over-input.The example shows that it eliminates the relevance among factors,reduces the input variables and improves the accuracy and efficiency. |
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| Keywords: | principal component analysis support vector machine seamfloor water inrush prediction |
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