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

Ship motion (SHM) forecasting value is an important parameter for ship navigation and operation. However, due to the coupling effect of wind, wave, and current, its time series has strong nonlinear characteristics, so it is a great challenge to obtain accurate forecasting results. Therefore, conside...

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Veröffentlicht in:	Nonlinear dynamics Jg. 107; H. 3; S. 2447 - 2467
Hauptverfasser:	Li, Ming-Wei, Xu, Dong-Yang, Geng, Jing, Hong, Wei-Chiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.02.2022 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Automotive Engineering Classical Mechanics Coding Control Deep learning Dynamical Systems Engineering Forecasting Machine learning Mathematical models Mechanical Engineering Optimization Optimization algorithms Original Paper Parameters Quantum theory Ship motion Time series Vibration Wind effects Ship motion forecasting Empirical mode decomposition Quantum computation Deep learning model Butterfly optimization algorithm
ISSN:	0924-090X, 1573-269X
Online-Zugang:	Volltext
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