A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm-Reference-Cited by-同舟云学术

A ship motion forecasting approach based on empirical mode decomposition method hybrid deep learning network and quantum butterfly optimization algorithm

Published:2022-01-19 Issue:3 Volume:107 Page:2447-2467
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Li Ming-Wei,Xu Dong-Yang,Geng Jing,Hong Wei-Chiang^ORCID

Funder

National Natural Science Foundation of China

Heilongjiang Province Natural Science Fund

Fundamental Research Funds for the Central Universities

Open Fund of the State Key Laboratory of Coastal and Offshore Engineering

Heilongjiang Sanjiang Project Administration Scientific Research and Experiments

Jiangsu Normal University, China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-021-07139-y.pdf

Reference47 articles.

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3. Kaplan, P.: A study of forecasting techniques for aircraft carrier motions at sea. J. Hydronaut. 3, 121–131 (1968). https://doi.org/10.2514/3.62814

4. Sidar, M., Doolin, B.: On the feasibility of real-time forecasting of aircraft carrier motion at sea. IEEE Trans. Autom. Control 28, 350–356 (1983). https://doi.org/10.1109/TAC.1983.1103227

5. Triantafyllou, M.S., Bodson, M.: Real time forecasting of marine vessel motions, using kalman filtering techniques. In: Offshore Technology Conference, 1982, pp. 159–173. https://doi.org/10.4043/4388-MS

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