Based on optimizing the diversity of component learners, this paper puts forward a method of traffic state prediction NNPDAP. In this paper, the perturbation training data set, perturbation input attribute and perturbation learning parameter are used to construct eight perturbation modes for optimizing the diversity of component learners. There have built three groups of experiments respectively for comparing the accuracy of traffic state prediction, error distribution, and time efficiency. The experimental results show that, by enhancing the diversity of component learners can improve the accuracy of prediction, so this method has a stronger competitiveness compared with no perturbation method.


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    Titel :

    Optimized Component Learners Diversity of Traffic State Forecasting Model with Multimode Perturbation


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wang, Wuhong (Herausgeber:in) / Wu, Jianping (Herausgeber:in) / Jiang, Xiaobei (Herausgeber:in) / Li, Ruimin (Herausgeber:in) / Zhang, Haodong (Herausgeber:in) / Liu, Qingchao (Autor:in) / Xu, Tianyu (Autor:in) / Li, Chun (Autor:in) / Nie, Shiqi (Autor:in)

    Kongress:

    International Conference on Green Intelligent Transportation System and Safety ; 2021 November 19, 2021 - November 21, 2021



    Erscheinungsdatum :

    2022-10-28


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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