Hao Han, Wei Wang*
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1353-1370, 2023, DOI:10.32604/cmes.2022.021494
- 31 August 2022
Abstract Accurate prediction of ship motion is very important for ensuring marine safety, weapon control, and aircraft carrier
landing, etc. Ship motion is a complex time-varying nonlinear process which is affected by many factors. Time
series analysis method and many machine learning methods such as neural networks, support vector machines
regression (SVR) have been widely used in ship motion predictions. However, these single models have certain
limitations, so this paper adopts a multi-model prediction method. First, ensemble empirical mode decomposition
(EEMD) is used to remove noise in ship motion data. Then the random forest (RF) prediction More >
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