Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network-Reference-Cited by-同舟云学术

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Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network

Published:2021-05-08 Issue: Volume: Page:
ISSN:0948-4280
Container-title:Journal of Marine Science and Technology
language:en
Short-container-title:J Mar Sci Technol

Author:

Jiang Yan,Hou Xian-Rui,Wang Xue-Gang,Wang Zi-Hao,Yang Zhao-Long,Zou Zao-Jian

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials,Ocean Engineering,Oceanography

Link

https://link.springer.com/content/pdf/10.1007/s00773-021-00819-9.pdf

Reference27 articles.

1. IMO (2002) Standards for Ship Manoeuvrability. Resolution MSC. 137(76)

2. Yin JC, Zou ZJ, Xu F (2015) Parametric identification of Abkowitz model for ship maneuvering motion by using partial least squares regression. J Offshore Mech Arct Eng 137:31301

3. Banazadeh A, Ghorbani MT (2013) Frequency domain identification of the Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel. Ocean Eng 72:344–355

4. Rajesh G, Bhattacharyya SK (2008) System identification for nonlinear maneuvering of large tankers using artificial neural network. Appl Ocean Res 30:256–263

5. Luo WL, Zou ZJ (2009) Parametric identification of ship maneuvering models by using support vector machines. J Ship Res 53:19–30

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