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Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm

Changyuan Chen (UGent) , Manasés Tello Ruiz (UGent) , Evert Lataire (UGent) , Guillaume Delefortrie (UGent) , Marc Mansuy (UGent) , Tianlong Mei and Marc Vantorre (UGent)
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Abstract
In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion's model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data.
Keywords
ship motions model, NLSSVM, BAS, parameter identification

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MLA
Chen, Changyuan, et al. “Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm.” PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, vol. 7B, AMER SOC MECHANICAL ENGINEERS, 2019.
APA
Chen, C., Tello Ruiz, M., Lataire, E., Delefortrie, G., Mansuy, M., Mei, T., & Vantorre, M. (2019). Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm. In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING (Vol. 7B). NEW YORK: AMER SOC MECHANICAL ENGINEERS.
Chicago author-date
Chen, Changyuan, Manasés Tello Ruiz, Evert Lataire, Guillaume Delefortrie, Marc Mansuy, Tianlong Mei, and Marc Vantorre. 2019. “Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm.” In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING. Vol. 7B. NEW YORK: AMER SOC MECHANICAL ENGINEERS.
Chicago author-date (all authors)
Chen, Changyuan, Manasés Tello Ruiz, Evert Lataire, Guillaume Delefortrie, Marc Mansuy, Tianlong Mei, and Marc Vantorre. 2019. “Ship Manoeuvring Model Parameter Identification Using Intelligent Machine Learning Method and the Beetle Antennae Search Algorithm.” In PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING. Vol. 7B. NEW YORK: AMER SOC MECHANICAL ENGINEERS.
Vancouver
1.
Chen C, Tello Ruiz M, Lataire E, Delefortrie G, Mansuy M, Mei T, et al. Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm. In: PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING. NEW YORK: AMER SOC MECHANICAL ENGINEERS; 2019.
IEEE
[1]
C. Chen et al., “Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm,” in PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING, Glasgow, Scotland, UK, 2019, vol. 7B.
@inproceedings{8639442,
  abstract     = {{In order to identify more accurately and efficiently the unknown parameters of a ship motions model, a novel Nonlinear Least Squares Support Vector Machine (NLSSVM) algorithm, whose penalty factor and Radial Basis Function (RBF) kernel parameters are optimised by the Beetle Antennae Search algorithm (BAS), is proposed and investigated Aiming at validating the accuracy and applicability of the proposed method, the method is employed to identify the linear and nonlinear parameters of the first-order nonlinear Nomoto model with training samples from numerical simulation and experimental data. Subsequently, the identified parameters are applied in predicting the ship motion. The predicted results illustrate that the new NLSSVM-BAS algorithm can be applied in identifying ship motion's model, and the effectiveness is verified. Compared among traditional identification approaches with the proposed method, the results display that the accuracy is improved. Moreover, the robust and stability of the NLSSVM-BAS are verified by adding noise in the training sample data.}},
  articleno    = {{V07BT06A028}},
  author       = {{Chen, Changyuan and Tello Ruiz, Manasés and Lataire, Evert and Delefortrie, Guillaume and Mansuy, Marc and Mei, Tianlong and Vantorre, Marc}},
  booktitle    = {{PROCEEDINGS OF THE ASME 38TH INTERNATIONAL CONFERENCE ON OCEAN, OFFSHORE AND ARCTIC ENGINEERING}},
  isbn         = {{9780791858851}},
  issn         = {{2153-4772}},
  keywords     = {{ship motions model,NLSSVM,BAS,parameter identification}},
  language     = {{eng}},
  location     = {{Glasgow, Scotland, UK}},
  pages        = {{9}},
  publisher    = {{AMER SOC MECHANICAL ENGINEERS}},
  title        = {{Ship manoeuvring model parameter identification using intelligent machine learning method and the beetle antennae search algorithm}},
  url          = {{http://dx.doi.org/10.1115/OMAE2019-95565}},
  volume       = {{7B}},
  year         = {{2019}},
}

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