Ship motion identification model based on enhanced Bi-LSTM

Ship motion identification model based on enhanced Bi-LSTM

ObjectiveAiming at the low prediction precision and poor adaptability of ship models based on the data-driven modeling strategy, an enhanced bi-directional long short-term memory (Bi-LSTM) model is proposed for the high-precision non-parametric modeling of ships. MethodsFirst, the feature extraction...

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Bibliographic Details
Main Authors:	Haozhe ZHANG, Zhibo YANG, Xuguo JIAO, Chengxing LÜ, Peng LEI
Format:	Article
Language:	English
Published:	Editorial Office of Chinese Journal of Ship Research 2025-02-01
Series:	Zhongguo Jianchuan Yanjiu
Subjects:	identification (control systems) non-parametric modelling one-dimensional convolutional neural network (1d-cnn) bi-directional long short-term memory （bi-lstm）neural network multi-head self-attention mechanism
Online Access:	http://www.ship-research.com/en/article/doi/10.19693/j.issn.1673-3185.03740
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