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[1]陈巧军,余浩,李艳昌,等.基于KPCA-LSSVM的回采工作面瓦斯涌出量的预测*[J].中国安全生产科学技术,2024,20(4):78-84.[doi:10.11731/j.issn.1673-193x.2024.04.011]
　CHEN Qiaojun,YU Hao,LI Yanchang,et al.Prediction of gas emission quantity in mining face based on KPCA-LSSVM[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(4):78-84.[doi:10.11731/j.issn.1673-193x.2024.04.011]
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基于KPCA-LSSVM的回采工作面瓦斯涌出量的预测*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年4期
页码:
78-84
栏目:
职业安全卫生管理与技术
出版日期:
2024-04-30

文章信息/Info

Title:
Prediction of gas emission quantity in mining face based on KPCA-LSSVM
文章编号:
1673-193X(2024)-04-0078-07
作者:
陈巧军; 余浩; 李艳昌; 谭依佳; 李奕
（1.辽宁工程技术大学 安全科学与工程学院，辽宁 葫芦岛 125105；
2.辽宁工程技术大学 电子与信息工程学院，辽宁 葫芦岛 125105)
Author(s):
CHEN Qiaojun;  YU Hao;  LI Yanchang;  TAN Yijia;  LI Yi
(1.College of Safety Science and Engineering,Liaoning Technical University,Huludao Liaoning 125105,China;
2.School of Electronic and Information Engineering,Liaoning Technical University,Huludao Liaoning 125105,China)
关键词:
瓦斯涌出量的预测; 核主成分分析法（KPCA）; 最小二乘支持向量机（LSSVM）; 相对误差绝对值
Keywords:
prediction of gas emission quantity;  kernel principal component analysis (KPCA);  least squares support vector machine (LSSVM);  absolute relative error
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2024.04.011
文献标志码:
A
摘要:
为了提高瓦斯涌出量预测精度，针对瓦斯涌出量影响因素具有线性重叠、高维非线性等问题，提出使用核主成分分析法（KPCA）对影响因素进行非线性降维。选取沈阳某矿30组样本数据，以前24组数据作为训练集，后6组数据作为测试集，将确定后的核主成分作为最小二乘支持向量机（LSSVM）的输入变量，建立KPCA-LSSVM预测模型，将预测结果与PCA-LSSVM、LSSVM、多元非线性回归、KPCA-BP神经网络、PCA-BP神经网络以及BP神经网络预测结果进行对比。以最大相对误差绝对值作为模型预测精度的评价指标。研究结果表明：当选取前4个核主成分时，即达到模型训练要求。KPCA-LSSVM模型的预测最大相对误差绝对值为5.89%，预测精度均优于其他6种对比模型。研究结果可为实现瓦斯涌出量高精度预测提供参考。
Abstract:
In order to improve the prediction accuracy of gas emission quantity,aiming at the problems of linear overlapping and high-dimensional nonlinearity of the influencing factors of gas emission quantity,it was proposed to carry out the dimensionality reduction on the influencing factors by using the kernel principal component analysis (KPCA).Firstly,30 sets of sample data from a mine in Shenyang were selected,with the first 24 sets of data as the training set and the last 6 sets of data as the test set.Then the determined kernel principal components were used as the input variables of least squares support vector machine (LSSVM) to establish the KPCA-LSSVM prediction model,and the prediction results were compared with the prediction results of PCA-LSSVM,LSSVM,multivariate nonlinear regression,KPCA-BP neural network,PCA-BP neural network,and BP neural network.Finally,the maximum absolute relative error was used as the evaluation index of model prediction accuracy.The results show that the requirements of model training are met when the first four kernel principal components are selected.The maximum absolute relative error of prediction by the KPCA-LSSVM model is 5.89%.The prediction accuracies are all better than the other six comparison models.The research results can provide a reference for realizing the high accuracy prediction of gas emission quantity.

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备注/Memo

备注/Memo:
收稿日期： 2023-08-10
* 基金项目： 国家自然科学基金项目（52174183）；2023年国家级大学生创新创业训练项目（202310147003）
作者简介： 陈巧军，本科生，主要研究方向为矿井灾害防治与评价。
更新日期/Last Update: 2024-05-09