| 基于主成分和BP神经网络方法的湖南省汽车保有量预测 |
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| 引用本文： | 李吟,田亚平,李朝奎,周新邵.基于主成分和BP神经网络方法的湖南省汽车保有量预测[J].衡阳师专学报,2011(6):122-126. |
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| 作者姓名： | 李吟 田亚平 李朝奎 周新邵 |
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| 作者单位： | [1]湖南科技大学煤炭资源清洁利用与矿山环境保护湖南省重点实验室,湖南湘潭411201 [2]衡阳师范学院资源环境与旅游管理系,湖南衡阳421008 [3]湖南城市学院计算机科学系,湖南益阳413000 |
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| 基金项目： | 基金项目：湖南科技大学研究生创新基金（S100130） |
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| 摘 要： | 汽车保有量预测对城市交通的发展方向、城市交通的控制管理、城市道路的建设情况等都有直接的参考意义。本文通过分析影响城市汽车保有量的因素，通过参考部分参考文献，城区人口总数人均GDP、公路客运量等8个指标，首先采用主成分分析法将8个因素进行分析，然后建立BP神经网络模型对湖南省2006到2008年汽车保有量进行预测，预测结果分别为98．93万辆、122．18万辆、137．03万辆，与汽车保有量实际值94．64万辆、121．72万辆、142．67万辆很接近，预测精度比较高。这表明BP神经网络具有很强的学习与泛化能力，用于汽车保有量预测的可行性与有效性。
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| 关 键 词： | 汽车保有量 主成分分析 BP神经网络 预测 |
A Prediction of Vehicle Possession in Hunan Province Based on Principal Component and BP Neural Network |
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| LI Yin,TIAN Ya-ping,LI Chao-kui,ZHOU Xin-shao.A Prediction of Vehicle Possession in Hunan Province Based on Principal Component and BP Neural Network[J].Journal of Hengyang Normal University,2011(6):122-126. |
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| Authors: | LI Yin TIAN Ya-ping LI Chao-kui ZHOU Xin-shao |
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| Institution: | 1. Clean Use of Coal Resources with Mining Environmental Protection Laboratory in Hunan Province, Hunan University of Science and Technology, Xiangtan Hunan 411201, China 2. Deparment of Resoources, Environment and Tourism Management, Hengyang Normal University, Hengyang H unan 421008, China 3. Department of Computer Science, Hunan City University, Yiyang Hunan 413000, China) |
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| Abstract: | Prediction of car ownership has a direct reference significance for the the development of urban transportation and con struction of urban roads. By analyzing the impact factors of urban auto possession,this paper first analyzes 8 indicators such as urban population,GDP, road passenger traffic and so on determined by some references, then establish BP neural network model to predicts the vehicles possession in Hunan Province from 2006 to 2008. The figures of prediction is 989,300, 1,221,800 and 1,370,300 respectively in 2006, 2007 and 2008, which is very close to the real ownership of 946,400,1,217,200 and 1,426, 700 respectively. It shows the prediction is very accurate. This suggests that the BP neural network has very strong learning and generalization ability and can be employed in prediction of vehicle possession effectively. |
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| Keywords: | vehicle possession principal component analysis the BP neural network prediction |
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