摘要
根据实测巷道通风摩擦阻力系数数据的特点,建立了主成分分析PCA-BP神经网络预测模型。采用PCA法对影响巷道通风摩擦阻力系数的支护类型、断面形状、巷道宽、巷道高、支护部分周边长、巷道断面积和巷道长度7个因素进行降维。将降维后因素的贡献率进行排序筛选,得到3个主成分指标(F_(1)、F_(2)和F_(3)),作为BP神经网络输入层的神经元。利用实测数据对PCA-BP神经网络模型进行训练和测试,并将测试结果与支持向量机回归(SVM)模型和BP神经网络模型的测试结果进行对比,结果显示:全因素的BP神经网络预测模型和SVM预测模型的平均精度分别为92.9420%、93.0235%,而PCA-BP预测模型的平均精度达到了96.4325%。PCA-BP神经网络模型不但简化了网络结构,更提高了网络的泛化能力,使预测误差更小、精度更高,为更准确地获得巷道通风摩擦阻力系数提供了一种有效的方法。
In response to the characteristics of the measured data on the roadway ventilation frictional resistance coefficient,a Principle Component Analysis(PCA)-BP neural network prediction model is established.The PCA method is used to reduce the dimensionality for seven factors that affecting the roadway ventilation frictional resistance coefficient,such as support type,section shape,roadway width,roadway height,peripheral length of the supported part,roadway cross-sectional area,and roadway length.The contribution rates of the factors after dimensionality reduction are sorted and screened to obtain three principle component indicators(F_(1),F_(2)and F_(3)),which are used as neurons in the input layer of the BP neural network.The PCA-BP neural network model is trained and tested using measured data,and the test results are compared with the test resutls of the Support Vector Machine Regression(SVM)model and the BP neural network model.The results show that the average accuracies of the full-factor BP neural network prediction model and the SVM prediction model are 92.9420%and 93.0235%,respectively,while the average accuracy of the PCA-BP prediction model reaches 96.4325%.The PCA-BP neural network model not only simplifies the structure of the network,but also improves it’s generalization ability,which resulting in smaller prediction error and higher accuracy,and provides an effective method for obtaining the ventilation frictional resistance coefficient of the roadway more accurately.
作者
高科
吕航宇
戚志鹏
刘玉姣
GAO Ke;LYU Hangyu;QI Zhipeng;LIU Yujiao(College of Safety Science and Engineering,Liaoning Technical University,Huludao 125105,China;Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education,Huludao 125105,China)
出处
《矿业安全与环保》
CAS
北大核心
2024年第1期7-13,共7页
Mining Safety & Environmental Protection
基金
国家自然科学基金青年基金项目(52104194)。
关键词
矿井通风
巷道通风摩擦阻力系数
预测模型
PCA-BP神经网络
主成分分析
影响因素
mine ventilation
roadway ventilation frictional resistance coefficient
prediction model
PCA-BP neural network
principle component analysis
influencing factors
