[1]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,(04):461-466.[doi:10.13800/j.cnki.xakjdxxb.2017.0402]
 LI Shu-gang,MA Yan-yang,LIN Hai-fei,et al.Selection of gas emission prediction index based on factor analysis[J].Journal of Xi'an University of Science and Technology,2017,(04):461-466.[doi:10.13800/j.cnki.xakjdxxb.2017.0402]
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基于因子分析法的瓦斯涌出量预测指标选取(/HTML)
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西安科技大学学报[ISSN:1672-9315/CN:61-1434/N]

卷:
期数:
2017年04期
页码:
461-466
栏目:
出版日期:
2017-07-30

文章信息/Info

Title:
Selection of gas emission prediction index based on factor analysis
文章编号:
1672-9315(2017)04-0461-06
作者:
李树刚12马彦阳1林海飞12潘红宇12赵鹏翔12
1.西安科技大学 安全科学与工程学院,陕西 西安 710054; 2.教育部 西部矿井开采及灾害防治重点实验室,陕西 西安 710054
Author(s):
LI Shu-gang12MA Yan-yang1LIN Hai-fei12PAN Hong-yu12ZHAO Peng-xiang12
1.College of Safety Science and Engineering,Xi'an University of Science and Technology,Xi'an 710054,China; 2.Key Laboratory of Western Mine Exploration and Hazard Prevention,Ministry of Education,Xi'an 710054,China
关键词:
瓦斯涌出量 指标选取 因子分析 BP神经网络
Keywords:
Key words:gas emission index selection factor analysis BP neural network
分类号:
TD 712
DOI:
10.13800/j.cnki.xakjdxxb.2017.0402
文献标志码:
A
摘要:
为解决瓦斯涌出量预测过程中存在的预测指标过多而导致预测精度降低的问题,构建因子分析与BP神经网络相结合的瓦斯涌出量预测模型。采用SPSS因子分析法对瓦斯涌出量影响因素进行了分析降维,并对BP神经网络模型进行训练及预测。结果表明:因子分析能使BP神经网络的输入变量从10个降为3个有实际含义的因子,经因子分析后预测模型的预测速度及精度均高于未处理的样本数据,预测性能明显改善,其平均误差为3.8%,最大误差为4.9%,表明所采取瓦斯涌出量预测指标的选取方法是可行和有效的。
Abstract:
Abstract:In order to solve the problem of redundant prediction index which leads to low prediction precision in the gas emission prediction,a gas emission prediction model was built by factor analysis combined with BP neural network.SPSS factor analysis method was used to reduce the dimension of the gas emission factors,and the model was trained and predicted.The results show that the input data of BP neural network can be reduced from 10 to 3 with practical meaning by factor analysis,and the prediction speed and calculation accuracy are higher than those of the neural network having not underwent factor analysis,with the averaged biases of 3.8% and the maximum error of 4.9%.It is indicated that the index selection of gas emission prediction is feasible and effective.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金(51374236; 51474172); 陕西省科技新星专项项目(2014KJXX69) 通讯作者:李树刚(1963-),男,甘肃省会宁人,教授,博士生导师,E-mail:lisg@xust.edu.cn
更新日期/Last Update: 1900-01-01