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.
Prediction of Underwater Glider Depth-Averaged Current Velocities Based on VMD-SSA-LSSVM
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Kapitel : 195 ; 1976-1985
18.03.2022
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Prediction of Underwater Glider Depth-Averaged Current Velocities Based on VMD-SSA-LSSVM
British Library Conference Proceedings | 2022
|