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[1]卢国斌,李晓宇,祖秉辉,等.基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究[J].中国安全生产科学技术,2017,13(6):109-114.[doi:10.11731/j.issn.1673-193x.2017.06.018]
　LU Guobin,LI Xiaoyu,et al.Research on time-varying series forecasting of gas emission quantity based on EMD-MFOA-ELM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2017,13(6):109-114.[doi:10.11731/j.issn.1673-193x.2017.06.018]
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基于EMD-MFOA-ELM的瓦斯涌出量时变序列预测研究
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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6期
页码:
109-114
栏目:
现代职业安全卫生管理与技术
出版日期:
2017-06-30

文章信息/Info

Title:
Research on time-varying series forecasting of gas emission quantity based on EMD-MFOA-ELM
文章编号:
1673-193X(2017)-06-0109-06
作者:
卢国斌1; 2; 李晓宇1; 2; 祖秉辉3; 董建军3
1.辽宁工程技术大学 矿业学院，辽宁 阜新 123000；2.矿山热动力灾害与防治教育部重点实验室，辽宁 阜新 123000；3.辽宁工程技术大学 矿业技术学院，辽宁 葫芦岛 125100
Author(s):
LU Guobin1;  2;  LI Xiaoyu1;  2;  ZU Binghui3;  DONG Jianjun3
1. College of Mining, Liaoning Technical University, Fuxin Liaoning 123000, China; 2. Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Fuxin Liaoning 123000, China; 3. College of Mining Technology, Liaoning Technical University, Huludao Liaoning 125100, China
关键词:
绝对瓦斯涌出量; 经验模态分解; 修正果蝇算法; 极限向量机; 多尺度
Keywords:
absolute gas emission quantity;  empirical mode decomposition (EMD);  modified fruit fly optimization algorithm (MFOA);  extreme learning machine (ELM);  multi-scale
分类号:
TP183
DOI:
10.11731/j.issn.1673-193x.2017.06.018
文献标志码:
A
摘要:
为准确分析工作面绝对瓦斯涌出量的非平稳特征，实现瓦斯涌出量的准确预测，基于经验模态分解(EMD)、修正的果蝇优化算法(MFOA)和极限学习机(ELM)基本原理，构建瓦斯涌出量的EMD-MFOA-ELM多尺度时变预测模型。通过EMD将瓦斯涌出量时变序列进行深层次分解，获得多尺度本征模态函数(IMF)；采用MFOA-ELM对各IMF时变序列建立动态预测模型,等权叠加各预测值，得到模型最终预测结果。以晋煤某矿瓦斯涌出量监测时序样本为例进行研究分析，结果表明：EMD能充分挖掘出监测数据隐含信息，有效降低数据复杂度；该模型预测相对误差为0.024 3%~0.651 0%，平均值仅为0.252 6%，预测精度和泛化能力高于未经EMD分解模型，能很好地适用于非平稳时变序列预测。
Abstract:
In order to accurately analyze the non-stationary characteristics of the absolute gas emission quantity in the working face and realize the accurate forecasting of the gas emission quantity, based on the basic principles of empirical mode decomposition (EMD), modified fruit fly optimization algorithm (MFOA) and extreme learning machine (ELM), a multi-scale time-varying series forecasting model of gas emission quantity based on EMD-MFOA-ELM was established. The time-varying series of gas emission quantity was deeply decomposed to obtain the multi-scale intrinsic mode function (IMF) by using EMD, and a dynamic forecasting model was established for each IMF time-varying series by using MFOA-ELM, then the final forecasting results of the model were obtained by superimposing each forecasting result with the equal weight. Taking the time series samples of gas emission quantity obtained by monitoring in a certain coal mine of Jincheng Coal Group as example, it showed that EMD could fully discover the implicit information and effectively reduce the complexity of the monitoring data. The relative error of the forecasting model was 0.0243%-0.6510%, and the average value was only 0.2526%. The prediction accuracy and generalization ability of the model were higher than that without EMD decomposition, and it can be well applied to non-stationary time-varying series forecasting.

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备注/Memo

备注/Memo:
辽宁省教育厅项目(L2014126)；辽宁省自然科学基金项目（201602349）
更新日期/Last Update: 2017-07-11