PCA-PSO-LSSVM模型在瓦斯涌出量预测中的应用
|
摘要
为提高预测回采工作面瓦斯涌出量预测精度,采用主成分分析法(PCA)与粒子群算法(PSO)及最小二乘支持向量机(LS-SVM)相结合的方法,在样本数据的选择上吸取主成分分析数据降维的优势,使所选择的数据样本简洁并且更具代表性.充分利用支持向量机训练速度快、能够获得全局最优解且拥有良好泛化性的特点,将粒子群算法与之相结合,从而寻找最优参数.建立基于PCA和PSO-LS-SVM回采工作面瓦斯涌出量预测模型,并在实际中获得成功应用.研究结果表明:该预测模型预测的最大相对误差为2.35%,最小相对误差为0.30%,平均相对误差为1.28%,相较其他预测模型有着更强的泛化能力和更高的预测精度.
In order to improve the prediction precision of the gas emission of mining working face, this paper utilized principal component analysis(PCA) and particle swarm optimization(PSO) to optimize the coupling of the method of least squares support vector machine(LS-SVM), the advantages of the principal component analysis in the data reduction of the sample data are analyzed, making the selection of data sample simple and more representative. Full use of support vector machine training speed to obtain the global optimal solution and has a good generalization performance characteristics, combining genetic algorithm with its combination, searching for optimal parameters. The LS-SVM prediction model of the mining working face gas emission was established based on PCA-PSO, and was successfully applied in practice use. Research results show that the prediction model has the maximum relative error of 2.35%, the minimum relative error of 0.30%, and the average relative error of 1.28%. Compared with other prediction model, this model has better generalization ability and higher prediction precision.
In order to improve the prediction precision of the gas emission of mining working face, this paper utilized principal component analysis(PCA) and particle swarm optimization(PSO) to optimize the coupling of the method of least squares support vector machine(LS-SVM), the advantages of the principal component analysis in the data reduction of the sample data are analyzed, making the selection of data sample simple and more representative. Full use of support vector machine training speed to obtain the global optimal solution and has a good generalization performance characteristics, combining genetic algorithm with its combination, searching for optimal parameters. The LS-SVM prediction model of the mining working face gas emission was established based on PCA-PSO, and was successfully applied in practice use. Research results show that the prediction model has the maximum relative error of 2.35%, the minimum relative error of 0.30%, and the average relative error of 1.28%. Compared with other prediction model, this model has better generalization ability and higher prediction precision.
引文
[1]Abdel Kader R F.Genetically improved PSO algorithm for efficient data clustering[J].Second International Conference on Machine Learning&Computing IEEE,2010,10(11):71-75.
[2]付华,丰胜成,高振彪,等.基于双耦合算法的煤与瓦斯突出预测模型[J].中国安全科学学报,2018,28(3):84-89.FU Hua,FENG Shengcheng,GAO Zhenbiao,et al.Study on double coupling algorithm based model for coal and gas outburst prediction[J].China Safety Science Journal,2018,28(3):84-89.
[3]MA Ning,XU Wenji,WANG Xuyue,et al.Prediction method for surface finishing of spiral bevel gear tooth based on least square support vector machine[J].Journal of Central South University of Technology(English Edition),2011,18(3):685-689.
[4]付华,谢森,徐耀松,等.基于ACC-ENN算法的煤矿瓦斯涌出量动态预测模型研究[J].煤炭学报,2014,39(7):1 296-1 301.FU Hua,XIE Sen,XU Yaosong,et al.Gas emission dynamic prediction model of coal mine based on ACC-ENN algorithm[J].Journal of China Coal Society,2014,39(7):1 296-1 301.
[5]周西华,徐丽娜,董强,等.基于PCA-RBF网络的煤与瓦斯突出强度预测[J].辽宁工程技术大学学报(自然科学版),2017,36(12):1 246-1 250.doi:10.11956/j.issn.1008-0562.2017.12.003.ZHOU Xihua,XU Lina,DONG Qiang,et al.Prediction of coal gas outburst intensity based on PCA-RBF network.[J].Journal of Liaoning Technical University(Nature Science),2017,36(12):1 246-1 250.doi:10.11956/j.issn.1008-0562.2017.12.003.
[6]卢国斌,康晋恺,白刚,等.PCA-BP在回采工作面瓦斯涌出量预测中的应用[J].辽宁工程技术大学学报(自然科学版),2015,34(12):1 329-1 334.doi:10.11956/j.issn.1008-0562.2015.12.001.LU Guobin,KANG Jinkai,BAI Gang,et al.Application of PCA and BPto gas emission prediction of mining working face[J].Journal of Liaoning Technical University(Nature Science),2015,34(12):1 329-1 334.doi:10.11956/j.issn.1008-0562.2015.12.001.
[7]刘俊娥,安凤平,林大超,等.采煤工作面瓦斯涌出量的固有模态SVM建模预测[J].系统工程理论与实践,2013,33(2):506-509.LIU June,AN Fengping,LIN Dachao,et al.Prediction of gas emission from coal face by intrinsic mode SVM modeling[J].Systems Engineering-Theory&Practice,2013,33(2):506-509.
[8]HUANG G B,WANG D H,LAN Y.Extreme learning machines a survey[J].International Journal of Machines Learning and Cybernetics,2011,2(2):107-122.
[9]吕伏,梁冰,孙维吉,等.基于主成分回归分析法的回采工作面瓦斯涌出量预测[J].煤炭学报,2012,37(1):113-116.LV Fu,LIANG Bing,SUN Weiji,et al.Gas emission quantity prediction of working face based on principal component regression analysis method[J].Journal of China Coal Society,2012,37(1):113-116.
[10]毕建武,贾进章.基于SPSS的PCA-MRA回采工作面瓦斯涌出量预测[J].安全与环境学报,2014,14(5):54-57.BI Jianwu,JIA Jinzhang.Prediction of gas emission quantity in the working face based on SPSS PCA-MRA[J].Journal of Safety and Environment,2014,14(5):54-57.
[11]洪林,赫祥林,董晓雷,等.PCA-GA-ELM煤矿瓦斯涌出量预测[J].辽宁工程技术大学学报(自然科学版),2015,34(7):779-784.doi:10.11956/j.issn.1008-0562.2015.07.003.HONG Lin,HE Xianglin,DONG Xiaolei,et al.Prediction of mine gas emission based on PCA-GA-ELM[J].Journal of Liaoning Technical University(Nature Science),2015,34(7):779-784.doi:10.11956/j.issn.1008-0562.2015.07.003.
[12]刘晶,翁公羽,付华.基于PSO-ENN算法的高压直流输电线路故障测距[J].高压电器,2016,52(9):153-157,163.LIU Jing,WENG Gongyu,FU Hua.Fault location on HVDCtransmission line based on PSO-ENN algorithm[J].High Voltage Apparatus,2016,52(9):153-157,163.
[13]谢国民,单敏柱,付华.基于FOA-SVM的煤矿瓦斯爆炸风险模式识别[J].控制工程,2018,25(10):1 859-1 864.XIE Guomin,SHAN Minzhu,FU Hua.Pattern recognition of gas explosion risks in coal mines based on FOA-SVM[J].Control Engineering of China,2018,25(10):1 859-1 864.
[14]付华,谢森,徐耀松,等.基于MPSO-WLS-SVM的矿井瓦斯涌出量预测模型研究[J].中国安全科学学报,2013,23(5):56-61.FU Hua,XIE Sen,XU Yaosong,et al.Study on MPSO-WLS-SVM-based Mine Gas Emission Prediction Model[J].China Safety Science Journal,2013,23(5):56-61.
[15]张强,贾宝山,董晓雷,等.PCA-GA-SVM的回采工作面瓦斯涌出量预测[J].辽宁工程技术大学学报(自然科学版),2015,34(5):572-577.doi:10.11956/j.issn.1008-0562.2015.05.006.ZHANG Qiang,JIA Baoshan,DONG Xiaolei,et al.Working face gas emission prediction based on PCA-GA-SVM[J].Journal of Liaoning Technical University(Nature Science),2015,34(5):572-577.doi:10.11956/j.issn.1008-0562.2015.05.006.
[16]付华,刘汀,张胜强,等.基于SOM-RBF算法的瓦斯涌出量动态预测模型研究[J].传感技术学报,2015,28(8):1 255-1 261.FU Hua,LIU Ting,ZHANG Shenqiang,et al.Gas emission quantity dynamic prediction model of coal Mine based on SOM-RBFalgorithm[J].Chinese Journal of Sensors and Actuators,2015,28(8):1 255-1 261.
[2]付华,丰胜成,高振彪,等.基于双耦合算法的煤与瓦斯突出预测模型[J].中国安全科学学报,2018,28(3):84-89.FU Hua,FENG Shengcheng,GAO Zhenbiao,et al.Study on double coupling algorithm based model for coal and gas outburst prediction[J].China Safety Science Journal,2018,28(3):84-89.
[3]MA Ning,XU Wenji,WANG Xuyue,et al.Prediction method for surface finishing of spiral bevel gear tooth based on least square support vector machine[J].Journal of Central South University of Technology(English Edition),2011,18(3):685-689.
[4]付华,谢森,徐耀松,等.基于ACC-ENN算法的煤矿瓦斯涌出量动态预测模型研究[J].煤炭学报,2014,39(7):1 296-1 301.FU Hua,XIE Sen,XU Yaosong,et al.Gas emission dynamic prediction model of coal mine based on ACC-ENN algorithm[J].Journal of China Coal Society,2014,39(7):1 296-1 301.
[5]周西华,徐丽娜,董强,等.基于PCA-RBF网络的煤与瓦斯突出强度预测[J].辽宁工程技术大学学报(自然科学版),2017,36(12):1 246-1 250.doi:10.11956/j.issn.1008-0562.2017.12.003.ZHOU Xihua,XU Lina,DONG Qiang,et al.Prediction of coal gas outburst intensity based on PCA-RBF network.[J].Journal of Liaoning Technical University(Nature Science),2017,36(12):1 246-1 250.doi:10.11956/j.issn.1008-0562.2017.12.003.
[6]卢国斌,康晋恺,白刚,等.PCA-BP在回采工作面瓦斯涌出量预测中的应用[J].辽宁工程技术大学学报(自然科学版),2015,34(12):1 329-1 334.doi:10.11956/j.issn.1008-0562.2015.12.001.LU Guobin,KANG Jinkai,BAI Gang,et al.Application of PCA and BPto gas emission prediction of mining working face[J].Journal of Liaoning Technical University(Nature Science),2015,34(12):1 329-1 334.doi:10.11956/j.issn.1008-0562.2015.12.001.
[7]刘俊娥,安凤平,林大超,等.采煤工作面瓦斯涌出量的固有模态SVM建模预测[J].系统工程理论与实践,2013,33(2):506-509.LIU June,AN Fengping,LIN Dachao,et al.Prediction of gas emission from coal face by intrinsic mode SVM modeling[J].Systems Engineering-Theory&Practice,2013,33(2):506-509.
[8]HUANG G B,WANG D H,LAN Y.Extreme learning machines a survey[J].International Journal of Machines Learning and Cybernetics,2011,2(2):107-122.
[9]吕伏,梁冰,孙维吉,等.基于主成分回归分析法的回采工作面瓦斯涌出量预测[J].煤炭学报,2012,37(1):113-116.LV Fu,LIANG Bing,SUN Weiji,et al.Gas emission quantity prediction of working face based on principal component regression analysis method[J].Journal of China Coal Society,2012,37(1):113-116.
[10]毕建武,贾进章.基于SPSS的PCA-MRA回采工作面瓦斯涌出量预测[J].安全与环境学报,2014,14(5):54-57.BI Jianwu,JIA Jinzhang.Prediction of gas emission quantity in the working face based on SPSS PCA-MRA[J].Journal of Safety and Environment,2014,14(5):54-57.
[11]洪林,赫祥林,董晓雷,等.PCA-GA-ELM煤矿瓦斯涌出量预测[J].辽宁工程技术大学学报(自然科学版),2015,34(7):779-784.doi:10.11956/j.issn.1008-0562.2015.07.003.HONG Lin,HE Xianglin,DONG Xiaolei,et al.Prediction of mine gas emission based on PCA-GA-ELM[J].Journal of Liaoning Technical University(Nature Science),2015,34(7):779-784.doi:10.11956/j.issn.1008-0562.2015.07.003.
[12]刘晶,翁公羽,付华.基于PSO-ENN算法的高压直流输电线路故障测距[J].高压电器,2016,52(9):153-157,163.LIU Jing,WENG Gongyu,FU Hua.Fault location on HVDCtransmission line based on PSO-ENN algorithm[J].High Voltage Apparatus,2016,52(9):153-157,163.
[13]谢国民,单敏柱,付华.基于FOA-SVM的煤矿瓦斯爆炸风险模式识别[J].控制工程,2018,25(10):1 859-1 864.XIE Guomin,SHAN Minzhu,FU Hua.Pattern recognition of gas explosion risks in coal mines based on FOA-SVM[J].Control Engineering of China,2018,25(10):1 859-1 864.
[14]付华,谢森,徐耀松,等.基于MPSO-WLS-SVM的矿井瓦斯涌出量预测模型研究[J].中国安全科学学报,2013,23(5):56-61.FU Hua,XIE Sen,XU Yaosong,et al.Study on MPSO-WLS-SVM-based Mine Gas Emission Prediction Model[J].China Safety Science Journal,2013,23(5):56-61.
[15]张强,贾宝山,董晓雷,等.PCA-GA-SVM的回采工作面瓦斯涌出量预测[J].辽宁工程技术大学学报(自然科学版),2015,34(5):572-577.doi:10.11956/j.issn.1008-0562.2015.05.006.ZHANG Qiang,JIA Baoshan,DONG Xiaolei,et al.Working face gas emission prediction based on PCA-GA-SVM[J].Journal of Liaoning Technical University(Nature Science),2015,34(5):572-577.doi:10.11956/j.issn.1008-0562.2015.05.006.
[16]付华,刘汀,张胜强,等.基于SOM-RBF算法的瓦斯涌出量动态预测模型研究[J].传感技术学报,2015,28(8):1 255-1 261.FU Hua,LIU Ting,ZHANG Shenqiang,et al.Gas emission quantity dynamic prediction model of coal Mine based on SOM-RBFalgorithm[J].Chinese Journal of Sensors and Actuators,2015,28(8):1 255-1 261.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |