Depth-averaged current velocities (DACVs) play an important role in marine scientific research, especially for navigation and path planning of autonomous underwater gliders (AUG). In this paper, we propose a DACV prediction model based on variational mode decomposition (VMD), sparrow search algorithm (SSA) and least squares support vector machine (LSSVM). Considering the characteristics of DACVs, we regard DACVs as time-series and use the VMD to decompose the time-series into several sub-series. In order to improve the accuracy of the prediction model, the SSA algorithm is used to optimize the parameters of LSSVM. Then use the optimized LSSVM to predict each sub-series, and the forecast result is the sum of each sub-series. Based on a simulated ocean current environment, we carried out four sets of comparative experiments. Experimental results show that VMD-SSA-LSSVM has high forecast accuracy and inherent robustness.


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    Titel :

    Prediction of Underwater Glider Depth-Averaged Current Velocities Based on VMD-SSA-LSSVM


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Hu, Hao (Autor:in) / Zhou, Yongjian (Autor:in) / Peng, Xingguang (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    18.03.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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