In recent years, with the development of water transportation, the number of shipping vessels has increased dramatically, the density of navigation has been increasing, and the size of the fleet has expanded. The purpose of this article is to build an artificial intelligence ship traffic hazard warning model based on BP neural network through accident analysis, in order to provide accurate and reliable information to the crew and ensure the safe and smooth navigation of the ship. This paper finishing 2010—2019 A harbor waters occurred collision avoidance traffic accident cases, select 10 vessels related to early warning indicators data as training samples for learning and training network to build early warning model, followed by the new four groups relevant early warning index data in the case of collision avoidance traffic accidents in the waters of port A were simulated and verified, and the average absolute error of the predicted value and the expected result value was 9.95%. The simulated prediction result of the model was close to the actual police level. The crew can help make faster decisions based on early warning, navigation safety and smooth flow of the waters of the ship.


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    Titel :

    Navigation and Collision Avoidance of Ships in Warning Area Based on Artificial Intelligence


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Chang, Jia-Wei (Herausgeber:in) / Yen, Neil (Herausgeber:in) / Hung, Jason C. (Herausgeber:in) / Wang, Yi (Autor:in) / Deng, Hua (Autor:in)

    Kongress:

    International Conference on Frontier Computing ; 2020 ; Singapore, Singapore July 10, 2020 - July 13, 2020


    Erschienen in:

    Frontier Computing ; Kapitel : 120 ; 1055-1062


    Erscheinungsdatum :

    01.01.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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