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[1]李润求,吴莹莹,施式亮,等.煤矿瓦斯涌出时序预测的自组织数据挖掘方法[J].中国安全生产科学技术,2017,13(7):18-23.[doi:10.11731/j.issn.1673-193x.2017.07.003]
 LI Runqiu,WU Yingying,SHI Shiliang,et al.Research on self-organizing data mining method for time series prediction of gas emission in coal mine[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(7):18-23.[doi:10.11731/j.issn.1673-193x.2017.07.003]
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煤矿瓦斯涌出时序预测的自组织数据挖掘方法
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
13
期数:
2017年7期
页码:
18-23
栏目:
学术论著
出版日期:
2017-07-31

文章信息/Info

Title:
Research on self-organizing data mining method for time series prediction of gas emission in coal mine
文章编号:
1673-193X(2017)-07-0018-06
作者:
李润求12吴莹莹1施式亮12朱红萍3
(1.湖南科技大学 资源环境与安全工程学院, 湖南 湘潭 411201; 2.煤矿安全开采技术湖南省重点实验室, 湖南 湘潭 411201; 3.湖南科技大学 信息与电气工程学院, 湖南 湘潭 411201)
Author(s):
LI Runqiu12 WU Yingying1 SHI Shiliang12 ZHU Hongping3
(1. School of Resource, Environment and Safety Engineering, Hunan University of Science & Technology, Xiangtan Hunan 411201, China; 2. Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Xiangtan Hunan 411201, China; 3. School of Information and Electrical Engineering, Hunan University of Science & Technology, Xiangtan Hunan 411201, China)
关键词:
瓦斯时间序列预测相空间重构(PSR)自组织数据挖掘(SODM )煤矿
Keywords:
gas time series prediction phase space reconstruction (PSR) self-organizing data mining (SODM) coal mine
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2017.07.003
文献标志码:
A
摘要:
为分析煤矿瓦斯涌出复杂系统时间序列预测方法,提出自组织数据挖掘(SODM)与相空间重构(PSR)相结合的预测建模方法。首先应用C-C方法计算时间序列的最佳嵌入维数和延迟时间后进行PSR;然后以二元二次方程为传递函数,以嵌入维数变量为自变量,以延迟时间后的时间序列为因变量,通过内准则确定传递函数系数和外准则选择最优传递函数,并以最优传递函数的输出为下层迭代传递函数的输入,最后获得最优复杂度预测模型。算例结果表明:该方法对煤矿瓦斯涌出量预测的相对误差为-5.751 7% ~6.049 3%,平均相对误差2.145 7%,预测结果能满足煤矿安全生产实际工程应用要求。
Abstract:
In order to analyze the time series prediction method for complex system of gas emission in coal mine, a prediction modeling method was proposed which combined the self-organizing data mining (SODM) with the phase space reconstruction (PSR). Firstly, the PSR was carried out after calculating the optimal embedding dimension and delay time of time series by using the C-C method. Secondly, the coefficients of transfer function were determined through the internal criteria, and the optimal transfer function was selected through the external criteria by taking the binary quadratic equations as the transfer function, taking the embedding dimension variable as the independent variable, and taking the time series after the delay time as the dependent variable, then the output of the optimal transfer function was taken as the input of the underlying iterative transfer function. Finally, the optimal complexity prediction model was established. The case results showed that the relative error of this method for the prediction of gas emission in coal mine was -5.7517% - 6.0493%, and the average relative error was 2.1457%. The prediction results can satisfy the application requirements of work safety in practical engineering of coal mine.

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(51274100);湖南省教育厅科学研究项目(14C0424,14B058);国家安全生产监督管理局南方煤矿瓦斯与顶板灾害预防控制安全生产重点实验室开放基金项目(E21727)
更新日期/Last Update: 2017-08-21