A BP neural network model was employed to forecast the railway freight turnover. First, this paper analyses the data of railway freight turnover in China from 1998 to 2012, build a three layers BP neural network, then by training and learning, a well-trained network can be used for simulating and forecasting. Finally, predict by the Grey GM(1,1) model and well-trained BP neural network respectively, and compares the errors of two prediction model, the results show that predicting the railway freight turnover by BP neural network has higher precision.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Railway Freight Turnover Forecast Based on the BP Neural Network



    Erschienen in:

    Erscheinungsdatum :

    2014-04-04


    Format / Umfang :

    4 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Railway freight volume forecast based on Hybird Algorithms

    Tang, Shijie / Zhang, Donglei | IEEE | 2022


    Railway Freight Volume Forecast Based on GRA-WD-WNN

    Tian, Wan qi / Zhao, Peng / Qiao, Ke | IEEE | 2019


    Railway freight volume forecast using an ensemble model with optimised deep belief network

    Feng, Fenling / Li, Wan / Jiang, Qiwei | Wiley | 2018

    Freier Zugriff

    BP Neural Network Based Optimization for China Railway Freight Transport Network

    Sun, Yan ;Lang, Mao Xiang ;Wang, Dan Zhu | Trans Tech Publications | 2014


    Research on Railway Freight Volume Prediction Based on Neural Network

    Yang Can / Li Xuemei | DOAJ | 2020

    Freier Zugriff