首页|基于KPCA-LSSVM的回采工作面瓦斯涌出量的预测

基于KPCA-LSSVM的回采工作面瓦斯涌出量的预测

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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.
Prediction of gas emission quantity in mining face based on KPCA-LSSVM
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 dimen-sionality 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 vec-tor 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 pre-diction accuracy.The results show that the requirements of model training are met when the first four kernel principal compo-nents 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.

prediction of gas emission quantitykernel principal component analysis(KPCA)least squares support vector machine(LSSVM)absolute relative error

陈巧军、余浩、李艳昌、谭依佳、李奕

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辽宁工程技术大学安全科学与工程学院,辽宁葫芦岛 125105

辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛 125105

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

国家自然科学基金国家级大学生创新创业训练计划(2023)

52174183202310147003

2024

10.11731/j.issn.1673-193x.2024.04.011

中国安全生产科学技术
中国安全生产科学研究院

中国安全生产科学技术

CSTPCD北大核心
影响因子：1.119
ISSN：1673-193X
年,卷(期)：2024.20(4)
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