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

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(chaosadaptive 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.

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.