Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks

被引：0

作者：

Danny D’Agostino

Andrea Serani

Frederick Stern

Matteo Diez

机构：

[1] CNR-INM,IIHR

[2] National Research Council-Institute of Marine Engineering,Hydroscience and Engineering

[3] National University of Singapore,undefined

[4] The University of Iowa,undefined

来源：

Journal of Ocean Engineering and Marine Energy | 2022年 / 8卷

关键词：

Nowcasting; Real-time short-term prediction; Recurrent neural networks; Long short-term memory networks; Gated recurrent units; Ship motion prediction;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：479 / 487

页数：8

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