回采巷道围岩稳定性预测模型及其成熟度
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摘要
针对我国煤矿井下回采巷道围岩稳定性预测方法不够成熟、准确率较低的问题,引入支持向量机算法,将回采巷道围岩稳定性的评价指标作为输入向量,稳定性等级作为输出向量,利用粒子群算法不断调整惩罚因子c、核参数g与单核系数λ1和λ2,建立了基于PSO-MKSVM的回采巷道围岩稳定性预测模型。定义出衡量回采巷道围岩稳定性预测模型的精度度量P和非均等代价下的代价度量E,并以成熟度度量M作为评价模型性能的准则。结果表明有多组λ1和λ2使得回采巷道围岩稳定性预测模型的成熟度度量达到最大值M=1,性能较稳定。
For the problems of immaturity and low accuracy of prediction methods of surrounding rock of roadway, we introduced support vector machine(SVM) algorithm and took the evaluation indexes of surrounding rock stability of roadway as the input and its stability levels as the output. A prediction model based on PSO-MKSVM was established by applying particle swarm optimization(PSO) algorithm to adjust the values of penalty factor c, kernel parameter g, and single kernel coefficient λ1 and λ2.The precision tolerance P and cost tolerance E under the unequal prices were defined to weigh the prediction results. Then the maturity tolerance M was confirmed as a criterion for assessing model performance. The experimental results showed that multiple sets of λ1 and λ2 values make the maturity tolerance M of prediction model of roadway surrounding rock stability reach the maximum value of 1, and the performance is stable.
For the problems of immaturity and low accuracy of prediction methods of surrounding rock of roadway, we introduced support vector machine(SVM) algorithm and took the evaluation indexes of surrounding rock stability of roadway as the input and its stability levels as the output. A prediction model based on PSO-MKSVM was established by applying particle swarm optimization(PSO) algorithm to adjust the values of penalty factor c, kernel parameter g, and single kernel coefficient λ1 and λ2.The precision tolerance P and cost tolerance E under the unequal prices were defined to weigh the prediction results. Then the maturity tolerance M was confirmed as a criterion for assessing model performance. The experimental results showed that multiple sets of λ1 and λ2 values make the maturity tolerance M of prediction model of roadway surrounding rock stability reach the maximum value of 1, and the performance is stable.
引文
[1]朱一丁,马文涛.回采巷道围岩分类的支持向量机方法[J].采矿与安全工程学报,2006,23(3):362-365.
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[6]石永奎,张景煜,王文正,等.回采巷道围岩稳定性的分类预测方法分析[J].煤炭技术,2015,34(7):46.
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[9]贺超峰,华心祝,马菁花.基于BP神经网络的回采巷道围岩稳定性分类[J].矿业工程研究,2012,27(3):6-9.
[10]崔亮,雷学峰,赵飞虎,等.基于加权平均法的围岩稳定性评价研究[J].煤炭工程,2011,42(6):77-78.
[11]周保生,朱维申,李术才.综放回采巷道围岩稳定性分类的研究[J].煤炭学报,2000,25(5):469-472.
[12]蔡世明.基于人工神经网络的回采巷道围岩稳定性分类及锚杆支护研究[D].重庆:重庆大学,2002.
[13]Han J,Kamber M.Data mining:concepts and techniques[M].San Francisco,USA:Morgan Kaufmann Publishers,2001.
[14]Lior Rokach.模式分类的基层方法[M].黄文龙,王晓丹,王毅,等,译.北京:国防工业出版社,2015.
[15]周志华.机器学习[M].北京:清华大学出版社,2016.
[2]李迎富.深井回采巷道围岩稳定性分类及其控制技术[J].煤矿安全,2013,44(6):75-78.
[3]曹锋,程永.神火矿区回采巷道围岩稳定性分类研究[J].煤矿开采,2006,11(6):65-70.
[4]赵汝星.基于随机森林的回采巷道围岩稳定性分类[J].煤矿安全,2014,45(11):200-202.
[5]张忠温,冯学武.回采巷道围岩稳定性分类及支护型式确定[J].采矿与安全工程学报,2002,19(2):73.
[6]石永奎,张景煜,王文正,等.回采巷道围岩稳定性的分类预测方法分析[J].煤炭技术,2015,34(7):46.
[7]朱志洁,张宏伟,陈蓥.基于PCA-RF的回采巷道围岩稳定性预测[J].水文地质工程地质,2015,42(6):108-113.
[8]杨科,谢广祥.基于ANN的综放回采巷道围岩稳定性预测研究[J].煤炭科学技术,2004,32(7):48-50.
[9]贺超峰,华心祝,马菁花.基于BP神经网络的回采巷道围岩稳定性分类[J].矿业工程研究,2012,27(3):6-9.
[10]崔亮,雷学峰,赵飞虎,等.基于加权平均法的围岩稳定性评价研究[J].煤炭工程,2011,42(6):77-78.
[11]周保生,朱维申,李术才.综放回采巷道围岩稳定性分类的研究[J].煤炭学报,2000,25(5):469-472.
[12]蔡世明.基于人工神经网络的回采巷道围岩稳定性分类及锚杆支护研究[D].重庆:重庆大学,2002.
[13]Han J,Kamber M.Data mining:concepts and techniques[M].San Francisco,USA:Morgan Kaufmann Publishers,2001.
[14]Lior Rokach.模式分类的基层方法[M].黄文龙,王晓丹,王毅,等,译.北京:国防工业出版社,2015.
[15]周志华.机器学习[M].北京:清华大学出版社,2016.