[1]徐 刚,王 磊,金洪伟,等.因子分析法与BP神经网络耦合模型对回采工作面瓦斯涌出量预测[J].西安科技大学学报,2019,(06):965-971.
 XU Gang,WANG Lei,JIN Hong-wei,et al.Gas emission prediction in mining face by Factor Analysis and BP neural network coupling model[J].Journal of Xi'an University of Science and Technology,2019,(06):965-971.
点击复制

因子分析法与BP神经网络耦合模型对回采工作面瓦斯涌出量预测(/HTML)
分享到:

西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2019年06期
页码:
965-971
栏目:
出版日期:
2019-12-20

文章信息/Info

Title:
Gas emission prediction in mining face by Factor Analysis and BP neural network coupling model
文章编号:
1672-9315(2019)06-0965-07
作者:
徐 刚12王 磊1金洪伟12刘沛东1
(1.西安科技大学 安全科学与工程学院,陕西 西安 710054; 2.西安科技大学 西部矿井开采及灾害防治教育部重点实验室,陕西 西安 710054)
Author(s):
XU Gang12WANG Lei1JIN Hong-wei12LIU Pei-dong1
(1.College of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an 710054,China; 2.Key Laboratory of Western Mine Exploitation and Hazard Prevention,Ministry of Education,Xi'an University of Science and Technology,Xi'an 710054,China)
关键词:
瓦斯涌出量预测 因子分析法 因子选取 BP神经网络 网络训练
Keywords:
gas emission prediction Factor Analysis factor selection BP neural network network training
分类号:
TD 712
文献标志码:
A
摘要:
针对工作面瓦斯涌出量的影响因素众多且难以筛选的问题,提出了基于因子分析法与BP神经网络的工作面瓦斯涌出量预测方法。首先运用因子分析法对矿井瓦斯涌出量的影响因素降维处理,并筛选出3个主因子作为BP神经网络的输入端神经元,然后构建出基于BP神经网络的工作面瓦斯涌出量预测模型,并进行网络训练,最后对预测模型的可靠性进行检验。结果表明:因子分析处理后变量作用在影响因子上的权重得到了重新分配,并且变量的维数得以减少,错综复杂的变量关系被优化成3个主因子之间的线性组合关系,使得BP神经网络模型预测的瓦斯涌出量结果更合理,精度更高; 工作面瓦斯涌出量预测值与实测值的相对误差均在5%以下,平均相对误差为3.25%,误差波动范围小,稳定性较好,为复杂因素影响下的工作面瓦斯涌出量预测提供了一条新的思路。
Abstract:
To solve the problem that the influence factors of gas emission in working face are numerous and difficult to screen,a prediction method of gas emission in facewas proposedbased on factor analysis method and BP neural network.Firstly,the factor analysis method was used to reduce the influence factors of mine gas emission with three main factors selected as the input neurons of BP neural network,and the prediction model of gas emission in working facewas constructedbased on BP Neural network with the network training carried out preformed.The reliability of the prediction model was finally tested.The results showed that the weight of the variable situated on the influence factor after factor analysis was redistributed,the dimension of the variable was reduced,and the complex variable relationship was optimized into alinear combination between the three main factors,which made the gas gushing result predicted by the BP neural network model more reasonable and the precision was higher.The relative error of the surface gas out prediction value and the measured value was below 5%,the average relative error was 3.25%,the error range was small,the stability was better,which provides a new way of thinking for the prediction of the surface gas gushing volume under the influence of complex factors.

参考文献/References:

[1] 王学萌,张继忠,王 荣.灰色系统分析及实用计算程序[M].武汉:华中科技大学出版社,2001 WANG Xue-meng,ZHANG Ji-zhong,WANG Rong.Grey system analysis and practical calculation program[M].Wuhan:Huazhong University of Science and Tec-hnology Press,2001. [2]曾 勇,吴财芳.矿井瓦斯涌出量预测的模糊分形神经网络研究[J].煤炭科学技术,2004,32(2):62-65. ZENG Yong,WU Cai-fang.Study on fuzzy fractal Neuralnetwork for prediction of gas emission in mine[J].Coal Science and Technology,2004,32(2):62-65. [3]Donald L,Turcottl.Fractals and chaos in geology and geophysics[M].Cambridge:Cambridge University Pre-ss,1997. [4]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,2004,35(10):73-75. YANG Zhi-yi,XIONG Ya-xuan,ZHANG Qian-lin.Prediction of neural network model for gas emission in working face[J].Coal Engineering,2004,35(10):73-75. [5]何 清.工作面瓦斯涌出量预测研究现状及发展趋势[J].矿业安全与环保,2016,43(4):98-101. HE Qing.Current status and development trend of gasemission prediction in working face[J].Mining Safety and Environmental Protection,2016,43(4):98-101. [6]张子戌,袁崇孚.瓦斯地质数学模型法预测矿井瓦斯涌出量研究[J].煤炭学报,1999,24(4):34-38. ZHANG Zi-xu,YUAN Chong-fu.Study on prediction of gas emission in mine by gas geological mathematical model method[J].Journal of China Coal Society,1999,24(4):34-38. [7]谷 松.预测瓦斯涌出量的新方法[C]//瓦斯地质与瓦斯防治进展.北京:中国煤炭学会,2007. GU Song.A new method for predicting gas emission[C]//Progress in gas geology and gas control.Beijing:China National Coal Society,2007. [8]张子敏.瓦斯地质学[M].徐州:中国矿业大学出版社,2009. ZHANG Zi-min.Gas geology[M].Xuzhou:China University of Mining and Technology Press,2009. [9]叶桢妮,侯恩科,段中会,等.郭家河煤矿回采工作面瓦斯涌出量预测[J].西安科技大学学报,2017,37(1):57-62. YE Zhen-ni,HOU En-ke,DUAN Zhong-hui,et al.Prediction of gas emission from mining face in Guojiahe coal mine[J].Journal of Xi'an University of Science and Technology,2017,37(1):57-62. [10]干欧亚.基于因子分析法的传媒上市公司财务绩效评价研究[D].成都:西南交通大学,2016. GAN Ou-ya.Research on financial performance evaluation of media listed companies based on factor analysis[D].Chengdu:Southwest Jiaotong University,2016. [11]毕建武,贾进章,刘 丹.基于SPSS多元回归分析的回采工作面瓦斯涌出量预测[J].安全与环境学报,2013,13(5):183-186. BI Jian-wu,JIA Jin-zhang,LIU Dan.Prediction of gas emission in mining face based on SPSS multiple regression analysis[J].Journal of Safety and Environment,2013,13(5):183-186. [12]郝天轩,宋 超.基于模糊神经网络的煤层瓦斯含量预测研究[J].中国安全科学学报,2011,21(8):36-41. HAO Tian-xuan,SONG Chao.Study on prediction of coal seam gas content based on fuzzy neural network[J].Chinese Journal of Safety Sciences,2011,21(8):36-41. [13]魏春荣,李艳霞,孙建华,等.灰色—分源预测法对煤矿瓦斯涌出量的应用研究[J].采矿与安全工程学报,2013,30(4):628-632. WEI Chun-rong,LI Yan-xia,SUN Jian-hua,et al.Study on the application of grey source prediction method to coal mine gas emission[J].Journal of Mining and Safety Engineering,2013,30(4):628-632. [14]王春娟,冯利华,罗 伟,等.基于主成分分析的BP神经网络对南京市水资源需求量预测[J].水资源与水工程学报,2012,23(6):6-9. WANG Chun-juan,FENG Li-hua,LUO Wei,et al.Prediction of water resources demand in Nanjing based on BP neural network based on principal component analysis[J].Journal of Water Resources and Water Engineering,2012,23(6):6-9. [15]Lunarzewski L.Gas emission prediction and recovery in underground coal mines[J].International Journal of Coal Geology,1998,35(1):117-145. [16]Edward Jackson J.A user's guide to principal components[M].New York:A Wiley-Interscience Publication,1992. [17]Horn J L.A rationale and test for the number of factors in factor analysis[J].Psychnmetrica,1965,30(2):179-185. [18]赵建军,贺宇航,黄润秋,等.基于因子分析法的边坡稳定性评价指标权重[J].西南交通大学学报,2015,50(2):325-330. ZHAO Jian-jun,HE Yu-hang,HUANG Run-qiu,et al.Evaluation index weight of slope stability based on factor analysis[J].Journal of Southwest Jiaotong University,2015,50(2):325-330. [19]Johnson R A,Wichern D W.Applied multivariate statistical analysis[M].New Jersey:Prentice-Hall,2014. [20]解素雯.基于主成分分析与因子分析数学模型的应用研究[D].淄博:山东理工大学,2016. XIE Su-wen.Research on the application of mathematical model based on principal component analysis and factor analysis[D].Zibo:Shandong University of Technology,2016. [21]Noack K.Control of gas emission in underground coal mines[J].International Journal of Coal Geology,1998,35(1):57-82. [22]李树刚,马彦阳,林海飞,等.基于因子分析法的瓦斯涌出量预测指标选取[J].西安科技大学学报,2017,37(4):461-466. LI Shu-gang,MA Yan-yang,LIN Hai-fei,et al.Selection of prediction index for gas emission based on factor analysis method[J].Journal of Xi'an University of Science and Technology,2017,37(4):461-466. [23]王生全,刘柏根,张召召,等.遗传算法的BP网络模型进行瓦斯涌出量预测[J].西安科技大学学报,2012,32(1):51-56. WANG Sheng-quan,LIU Bai-gen,ZHANG Zhao-zhao,et al.BP network model of genetic algorithm for gas emission prediction[J].Journal of Xi'an University of Science and Technology,2012,32(1):51-56.

备注/Memo

备注/Memo:
收稿日期:2018-12-06 责任编辑:杨泉林
基金项目:国家自然科学基金(51404189,51404190); 陕西省自然科学基础研究计划(2015JQ51191)
通信作者:徐 刚(1981-),男,河南南阳人,博士,副教授,E-mail:408247198@qq.com
更新日期/Last Update: 2019-12-20