| 基于PCA改进的LS—SVM公路旅游客流量预测模型 |
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| 引用本文： | 张朝元,王彭德,陈丽.基于PCA改进的LS—SVM公路旅游客流量预测模型[J].昆明理工大学学报(理工版),2011,36(5):38-42. |
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| 作者姓名： | 张朝元 王彭德 陈丽 |
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| 作者单位： | 1. 大理学院,数学与计算机学院,云南,大理,671003
2. 大理学院,工程学院,云南,大理,671003 |
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| 基金项目： | 云南省教育厅科学研究基金项目（项目编号：2010C140）. |
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| 摘 要： | 公路交通旅游客流量的影响因素众多，加大了预测模型输入变量的复杂性，降低了模型的运行速度和预测精确．首先，利用主成分分析对公路旅游客流量影响指标进行综合分析，得到主成分即输入变量，然后建立以主成分为输入变量，以客流量为输出变量的最小二乘支持向量机预测模型．通过实例验证和比较，展示了基于主成分分析改进的最小二乘支持向量机公路交通旅游客流量预测模型，具有较好的预测效果和较高的应用价值．
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| 关 键 词： | 主成分分析 最小二乘支持向量机 公路旅游客流量 预测模型 |
Forecast Model of Highway Traveling Passenger Volume Based on LS-SVM Improved by PCA |
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| ZHANG Chao-yuan,WANG Peng-de,CHEN Li.Forecast Model of Highway Traveling Passenger Volume Based on LS-SVM Improved by PCA[J].Journal of Kunming University of Science and Technology(Natural Science Edition),2011,36(5):38-42. |
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| Authors: | ZHANG Chao-yuan WANG Peng-de CHEN Li |
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| Affiliation: | 1. College of Mathematics and Computer, Dali University, Dali, Yunnan 671003, China; 2. College of Engineering, Dali University, Dali, Yunnan 671003, China) |
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| Abstract: | The fact that many factors have influence on highway traveling passenger volume increases the com- plexity of the input variables and reduces running speed and precision of the prediction model. Firstly, the main components are gained through analyzing the impact indicators of highway traveling passenger volume using principal component analysis. Secondly, the forecast model of LS - SVM （ Least Squares Support Vector Machine） is established by taking main components as input variables and passenger volume as output variable. Through example confirmation and comparison, it is shown that the forecast model of highway traveling passenger volume based on LS -SVM improved by principal component analysis has good forecast effect and high application value. |
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| Keywords: | principal component analysis LS -SVM highway traveling passenger volume forecast model |
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