| 康海贵,李明伟,周鹏飞,赵泽辉.基于混沌自适应遗传 ν SVR的城市客运量预测[J].,2012,(2):227-232 |
| 基于混沌自适应遗传 ν SVR的城市客运量预测 |
| Prediction of passenger traffic volume using ν support vector regression optimized by chaos adaptive genetic algorithm |
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| DOI：10.7511/dllgxb201202012 |
| 中文关键词: ν 支持向量回归机 遗传算法 混沌映射 自适应机制 客运量预测 |
| 英文关键词: ν support vector regression genetic algorithm chaos mapping adaptive mechanism passenger traffic volume prediction |
| 基金项目:高等学校博士学科点专项科研基金资助项目(200801411105)；河南省交通厅科技计划资助项目(200912). |
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| 中文摘要: |
| 针对城市客运量预测问题本身所存在的小样本、高维数和非线性等特点,将 ν 支持向量回归机( ν support vector regression, ν SVR)应用于城市客运量预测．为了提高 ν SVR模型的预测精度和泛化性能,利用基于混沌理论和自适应机制的混沌自适应遗传算法(chaos adaptive genetic algorithm,CAGA)优选 ν SVR模型参数,建立了基于CAGA进行参数优选的CAGA ν SVR城市客运量预测模型．结合1978~2008年统计数据进行了仿真预测,结果表明该模型的预测性能优于RBF神经网络模型、GA SVR模型和GA ν SVR模型,平均绝对相对误差控制在2.3%以内,可有效应用于城市客运量预测． |
| 英文摘要: |
| Aiming at the prediction of passenger traffic volume with small samples, multi dimension and nonlinearity, ν support vector regression ( ν -SVR) is introduced to forecast passenger traffic volume. To seek the optimal forecast accuracy and generalization performance of ν SVR, chaos adaptive genetic algorithm (CAGA) is used to optimize the parameter, which is based on chaos mapping and adaptive mechanism. Then, a new passenger traffic volume forecasting model of ν SVR named by CAGA ν SVR is proposed. The model is applied to forecasting passenger traffic volume with data of 1978 2008. Compared with RBF neural network model, GA SVR model and GA ν SVR model, it is concluded that CAGA ν SVR prediction model has higher prediction precision, and can effectively predict passenger traffic volume with less than 2.3% of mean absolute relative error. |
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