Prediction of Railway Passenger Traffic Volume Based on Neural Tree Model
The railway passenger traffic volume(RPTV) fore cast can offer scientific basis for the establishment of policy and making of transportation development plan.This paper applies the neural tree model for predicting the railway passenger traffic volume.The optimal structure is developed using the Improved Probabilistic Incremental Program Evolution (IPIPE) and the free parameters encoded in the optimal tree are op timized by the Particle Swarm Optimization(PSO) algorithm, and an improved sigmoid function is applied as the neural activation function,a new fitness function combines error and Occams razor is used for for balancing of accuracy and parsimony of evolved structures. Based on the RPTV from 1985 to 2007 of China,the performance and efficiency of the applied model are evaluated and compared with the multi-layer feed forward network(MLFN)and support vector machine(SVM).
railway passenger traffic volume neural tree improved probabilistic incremental program evolution particle swarm optimization Occams razor
Feng Qi Xiyu Liu Yinghong Ma
School of Management and Economics Shandong Normal University, SDNU Jinan, China
国际会议
长沙
英文
370-373
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）