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Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks
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Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks
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Journal Article
Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks
2022
Overview
The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state. Specifically, the performance of recurrent neural networks, long short-term memory, and gated recurrent units models are assessed and compared using a data set coming from computational fluid dynamics simulations of a self-propelled destroyer-type vessel in stern-quartering sea state 7. Time-series of incident wave, ship motions, rudder angle, as well as immersion probes, are used as variables for a nowcasting problem. The objective is to obtain about 20 s ahead prediction. Overall, the three methods provide promising and comparable results.
Publisher
Springer International Publishing,Springer Nature B.V
Subject
