Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 23, 2022
- Source ID
- 10.1007/s40722-022-00255-w
Entities
People
- Andrea Serani
- Danny D’agostino
- Frederick Stern
- Matteo Diez
Organizations
- Office of Naval Research Global