Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 27 May 2021.
Builds on
6 of 17 references.
References · 6
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2015A Theoretically Grounded Application of Dropout in Recurrent Neural Networkscited by 161
- 2019Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea statescited by 12
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.