| 基于RBF神经网络因子分析的汽车保有量预测 |
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| 引用本文： | 郭晶伟,何明.基于RBF神经网络因子分析的汽车保有量预测[J].交通科技与经济,2011,13(1):67-70. |
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| 作者姓名： | 郭晶伟 何明 |
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| 作者单位： | 1. 江苏省建设厅城市规划技术咨询中心,江苏南京,210036
2. 东南大学交通学院,江苏南京,210096 |
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| 摘 要： | 汽车保有量预测对城市交通的发展方向有直接的参考意义,通过分析影响城市汽车保有量的因素,采用因子分析法提炼出较少的线性无关的主要因素,建立预测城市汽车保有量的RBF神经网络模型.最后通过实例分析,对RBF神经网络因子分析法计算结果和全要素神经网络模拟结果比较,得出RBF神经网络因子分析法在运算效率、运算精度上的优越性.
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| 关 键 词： | 汽车保有量 预测 RBF神经网络 因子分析 |
Forecasting of Automobile Population by RBF Neural Network Factor Analysis |
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| GUO Jing-wei,HE Ming.Forecasting of Automobile Population by RBF Neural Network Factor Analysis[J].Technology & Economy in Areas of Communications,2011,13(1):67-70. |
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| Authors: | GUO Jing-wei HE Ming |
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| Institution: | 1. City Planning Consultation Center of Construction Department of Jiangsu Province, Nanjing 210036, China; 2. Transportation College, Southeast University, Nanjing 210096, China) |
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| Abstract: | The forecast of the vehicle ownership has the direct significance to the urban transportation development direction, by analyzing the effect to the urban vehicle ownership, factor analysis method is imposed to epurate lesser non-linear factors, and the RBF neural network model to forecast the urban auto- mobile population is established. Finally, through a case, the result by RBF neural network simulation factor analysis is compared with multinomial fitting RBF neural network simulation. The conclusion is that the method of RBF neural network simulation factor analysis is superior both in efficiency and precision. |
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| Keywords: | automobile population forecasting RBF neural network factor analysis |
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