| 改进的瓦斯突出预测模型 |
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| 引用本文： | 辛元芳,姜媛媛.改进的瓦斯突出预测模型[J].煤炭技术,2014(10):11-13. |
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| 作者姓名： | 辛元芳 姜媛媛 |
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| 作者单位： | 安徽理工大学电气与信息工程学院 |
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| 基金项目： | 安徽省高等学校自然科学研究项目(KJ2013B086) |
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| 摘 要： | 针对瓦斯突出时间序列的非平稳特性,提出了一种基于经验模态分解和极限学习机的瓦斯突出预测模型。以某矿井工作面实际采集的瓦斯浓度为例,仿真结果表明EMD-ELM模型在训练速度和预测精度上优于ELM和最小二乘支持向量机模型。
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| 关 键 词： | 瓦斯突出 预测 极限学习机 经验模态分解 |
Improved Gas Outburst Prediction Model |
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| XIN Yuan-fang;JIANG Yuan-yuan.Improved Gas Outburst Prediction Model[J].Coal Technology,2014(10):11-13. |
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| Authors: | XIN Yuan-fang;JIANG Yuan-yuan |
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| Affiliation: | XIN Yuan-fang;JIANG Yuan-yuan;College of Electrical and Information Engineering, Anhui University of Science and Technology; |
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| Abstract: | Gas outburst time series has the non-stationary characteristics,a kind of gas outburst prediction model which was based on extreme learning machine and empirical mode decomposition were put for ward.Taking a mine working face actual acquisition of gas concentration as an example, the simulation results show that the EMD- ELM model in prediction accuracy and the training speed is superior than ELM and LSSVM model. |
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| Keywords: | gas outburst prediction Extreme Learning Machine Empirical Mode Decomposition |
| 本文献已被 CNKI 等数据库收录！ |
