基于PCA-BP神经网络的铁路客运量预测模型研究

Research of Railway Passenger Volume Forecast Model Based on PCA-BP Neural Network

本文分析铁路客运量影响因素,利用主成分分析(PCA)消除原始铁路客运量影响因素之间的相关性,将主成分分析结果作为BP神经网络的输入,并通过增加动量项、输入数据处理、调整学习速率优化BP神经网络,提出基于PCA-BP神经网络的铁路客运量预测模型。实例研究表明,与BP神经网络相比,PCABP神经网络能有效提高铁路客运量预测精度。

In order to forecast railway passenger volume,this paper analyzes influence factors of railway passenger volume.Based on the principal component analysis（PCA） method,this paper proposes a PCA-BP neural network,which eliminates the relevant factors＇ correlation between original influence factors,sets the PCA results as the BP neural network＇s input,optimizes the BP neural network by increasing momentum item,input data processing,and adjust the learning rate.The case results indicate that,compared with the BP neural network,PCA-BP neural network can effectively improve the accuracy of railway passenger volume forecast.