基于GA-BP神经网络的淋水井筒风温预测模型  

Prediction model of airflow temperature of shaft with water dropping based on GA-BP Neural Network

作　　者：高佳南 马乐天 白金阳 田皓天 黄曾宁 梁春燕 康杰 GAO Jianan;MA Letian;BAI Jinyang;TIAN Haotian;HUANG Zengning;LIANG Chunyan;KANG Jie(College of Safety Science and Engineering,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]西安科技大学安全科学与工程学院,陕西西安710054

出　　处：《中国矿业》2023年第11期96-101,共6页China Mining Magazine

基　　金：国家自然科学基金项目资助(编号:51974232);陕西省教育厅一般专项科研计划项目资助(编号:21JK0758)。

摘　　要：矿井进风井筒井底风温是井下风流热计算的重要节点。为准确预测淋水井筒风温,利用皮尔逊相关系数分析与遗传算法(GA)优化BP神经网络相结合的预测模型。借助皮尔逊相关系数分析筛选其中3个主要特征变量作为BP神经网络的输入变量,利用GA优化BP神经网络的权值和阈值,并与标准BP神经网络预测模型进行比较。研究结果表明,全部特征变量与特征变量筛选输入的标准BP神经网络预测模型的预测结果的平均绝对百分比误差分别为1.25%和2.33%,GA优化BP神经网络预测模型的预测结果的平均绝对百分比误差分别为0.97%和2.21%,GA-BP神经网络预测模型预测精度高于标准BP神经网络预测模型,基于特征变量筛选的预测模型既保持了较高的预测精度,又提高了预测效率。The airflow temperature at the bottom of the mine intake shaft is an important node in the thermal calculation of underground airflow.In order to accurately predict the airflow temperature of shaft with water dropping,this paper utilized Pearson correlation coefficient analysis and genetic algorithm(GA)to optimize the coupling of the method of BP neural network.Using Pearson correlation coefficient analysis,three main feature variables are selected as input variables the BP neural network.GA is used to optimize the weights and thresholds of the BP neural network,and compared with the standard BP neural network prediction model.The research results show that the average absolute percentage errors of the prediction results of the standard BP neural network prediction model with all feature variables as input variables and the standard BP neural network prediction model based on feature variable screening as input variables are 1.25% and 2.33%,respectively,while the average absolute percentage errors of the prediction results of the GA optimized BP neural network prediction model are 0.97% and 2.21%,respectively.The GA-BP neural network prediction model has higher prediction accuracy than the standard BP neural network prediction model.The prediction model based on feature variable optimization maintains high prediction accuracy,it has also improved prediction efficiency.

关 键 词：淋水井筒 风温预测 皮尔逊相关系数 遗传算法 BP神经网络 

分 类 号：TD727[矿业工程—矿井通风与安全]