Research on Urban Car Ownership Prediction Based on PCA-BP Neural Network

Variation of urban car ownership is influenced by many factors,and relations between them are nonlinear.Because redundant information existing in the factors can not be eliminated effectively,prediction accuracy of the traditional mathematical model and neural network model is low.To improve the prediction accuracy of urban car ownership,the PCA-BP was proposed.The redundant information among the various factors was removed through principal component analysis on impact factors of urban ownership,the neural network topology structure was simplified,and the training speed and prediction accuracy were improved.The implementation results show that compared with ARIMA,BP and multiple regression analysis,the prediction accuracy of PCA-BP neural network mode is higher and the speed is faster.The method provides a new way for the urban car ownership production prediction.