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钟蒙,薛运强,周珣,等. 基于灰色-反向传播神经网络的江西省公路货运量预测[J]. 科学技术与工程, 2021, 21(24): 10478-10484.
Zhong Meng,Xue Yunqiang,Zhou Xun,et al.Forecast of Highway Freight Volume in Jiangxi Province Based on Grey-BP Neural Network[J].Science Technology and Engineering,2021,21(24):10478-10484.
基于灰色-反向传播神经网络的江西省公路货运量预测
Forecast of Highway Freight Volume in Jiangxi Province Based on Grey-BP Neural Network
投稿时间:2021-03-16  修订日期:2021-06-10
DOI:
中文关键词:  公路货运量预测  灰色关联度分析  反向传播神经网络
英文关键词:highway freight volume forecast  grey correlation analysis  back propagation neural network
基金项目:国家自然科学基金(71961006);江西省社科规划项目青年项目(18GL37);江西省交通厅规划办项目(2004520065)
              
作者单位
钟蒙 华东交通大学交通运输与物流学院
薛运强 华东交通大学交通运输与物流学院
周珣 江西省交通运输厅规划办公室
张兵 华东交通大学交通运输与物流学院
周丹丹 华东交通大学交通运输与物流学院
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中文摘要:
      鉴于反向传播(Back Propagation,BP)神经网络算法收敛速度慢、局部极小化、结构选择不一的问题,提出结合灰色关联度分析的BP神经网络方法进行公路货运量预测,提高了模型的非线性学习和泛化能力及预测精度。该预测模型以江西省为例,首先利用灰色关联度分析确定预测目标的影响因子;然后,将关联度强的第一产业、第二产业和人均GDP作为公路货运预测模型的自变量,公路货运量和自变量作为训练样本,BP神经网络模型通过正向计算传播,误差反向传播,训练神经网络;最后,该方法应用于江西省公路货运量实际预测中进行有效性验证,结果表明该方法非线性拟合效果较好,具有较高的预测精度。
英文摘要:
      In view of the problems of slow convergence speed, local minimization, and different structure choices of back propagation(BP) neural network algorithm, a BP neural network method combined with gray relational analysis was proposed to predict road freight volume, which improved the model's nonlinear learning ability, generalization ability and prediction accuracy. The data from Jiangxi Provincial Statistical Yearbook was used in this forecasting model. First, the gray correlation analysis was carried out to determine the impact factors of the forecast target. Then, the first industry, the second industry and the per capita GDP of strong correlation were used as the independent variables of the road freight forecast model. The freight volume and independent variables were used as training samples. The BP neural network model was propagated through forward calculation, and the error was backpropagated to train the neural network. Finally, the method is applied to the actual forecast of highway freight volume in Jiangxi Province to verify the validity. The results show that the method has better nonlinear fitting effect and higher prediction accuracy.
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