[1]吴 旋,来兴平,郭俊兵,等.综采面区段煤柱宽度的PSO-SVM预测模型[J].西安科技大学学报,2020,(01):64-70.
 WU Xuan,LAI Xing-ping,GUO Jun-bing,et al.PSO-SVM prediction model of coal pillar width in fully mechanized mining face[J].Journal of Xi'an University of Science and Technology,2020,(01):64-70.
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综采面区段煤柱宽度的PSO-SVM预测模型(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2020年01期
页码:
64-70
栏目:
出版日期:
2020-02-15

文章信息/Info

Title:
PSO-SVM prediction model of coal pillar width in fully mechanized mining face
文章编号:
1672-9315(2020)01-0064-07
作者:
吴 旋123来兴平123郭俊兵4崔 峰123王泽阳123许慧聪123
(1.西安科技大学 能源学院,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; 3.西安科技大学 榆林煤炭绿色安全高效开采与清洁利用研究院,陕西 榆林 719000; 4.西山煤电股份有限公司 马兰矿,山西 太原 030205)
Author(s):
WU Xuan123LAI Xing-ping123GUO Jun-bing4CUI Feng123WANG Ze-yang123XU Hui-cong123
(1.College of Energy 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; 3.Yulin Research Institute of Green,safe and efficient Mining and Clean Utilization of Coal,Xi'an University of Science and Technology,Yu'lin 719000,China; 4.Malan Coal Mine,Xishan Coal and Electric Power Co.,Ltd.,Taiyuan 030205,China)
关键词:
支持向量机 区段煤柱宽度 粒子群优化算法 预测
Keywords:
support vector machines width of sectional coal pillar particle swarm optimization prediction
分类号:
TD 313
文献标志码:
A
摘要:
为准确预测缓倾斜煤层区段煤柱宽度,分析了缓倾斜煤层综采工作面的主要影响因素,选取8个因子,建立了粒子群优化的支持向量机区段煤柱宽度预测模型(Particle Swarm Optimization Support Vector Machine,PSO-SVM),通过缓倾斜煤层的区段煤柱宽度情况统计分析,对粒子群优化的支持向量机模型(PSO-SVM)、网格搜索优化的支持向量机模型(GS-SVM)和遗传算法优化的支持向量机模型(GA-SVM)3种预测方法的精度进行了对比分析。结果表明:3种方法的预测平均相对误差PSO-SVM为1.81%,GS-SVM为8.36%,GA-SVM为3.78%.PSO-SVM模型有较高的预测精度和较强的普适性,能够相对精确、高效地预测缓倾斜煤层区段煤柱宽度,对缓倾斜煤层综采面区段煤柱宽度选取具有一定指导意义。
Abstract:
In order to accurately predict pillar width in gently inclined coal seam section,the main influencing factors of coal seam in gently inclined coal seam face are analyzed,and eight factors are selected.A particle swarm optimization support vector machine(PSO-SVM)based on particle swarm optimization(PSO)is established,and the section pillar width of gently inclined coal seam is statistically analyzed.The accuracy of three prediction methods:particle swarm optimization support vector machine model(PSO-SVM),grid search optimization support vector machine model(GS-SVM),and transmission algorithm optimized support vector machine model(GA-SVM)are compared and analyzed.Average relative errors predictive of triple prediction methods PSO-SVM,GS-SVM and GA-SVMare 1.81%,8.36% and 3.78%.PSO-SVM model has high prediction accuracy and strong universality,and can predict the width of coal pillar in the slow inclined coal seam section accurately and efficiently.It has certain guiding significance for the design width of coal pillar in the fully mechanized mining face of the slow inclined coal seam.

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备注/Memo

备注/Memo:
收稿日期:2019-10-12 责任编辑:刘 洁
基金项目:国家自然科学基金(51974236)
通信作者:陈晓坤(1961-),男,辽宁抚顺人,教授,博士生导师,E-mail:630797778@qq.com
更新日期/Last Update: 2020-02-15