Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.
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June 2016
Research-Article
Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization
Weilin Luo
,
Weilin Luo
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal;
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal;
College of Mechanical
Engineering and Automation,
Fuzhou University,
Fujian 350108, China
Engineering and Automation,
Fuzhou University,
Fujian 350108, China
Search for other works by this author on:
C. Guedes Soares
,
C. Guedes Soares
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt
Search for other works by this author on:
Zaojian Zou
Zaojian Zou
School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
Search for other works by this author on:
Weilin Luo
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal;
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal;
College of Mechanical
Engineering and Automation,
Fuzhou University,
Fujian 350108, China
Engineering and Automation,
Fuzhou University,
Fujian 350108, China
C. Guedes Soares
Centre for Marine Technology and
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt
Ocean Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Lisbon 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt
Zaojian Zou
School of Naval Architecture,
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
Ocean and Civil Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
1Corresponding author.
Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received March 30, 2013; final manuscript received January 21, 2016; published online April 6, 2016. Editor: Solomon Yim.
J. Offshore Mech. Arct. Eng. Jun 2016, 138(3): 031101 (8 pages)
Published Online: April 6, 2016
Article history
Received:
March 30, 2013
Revised:
January 21, 2016
Citation
Luo, W., Guedes Soares, C., and Zou, Z. (April 6, 2016). "Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization." ASME. J. Offshore Mech. Arct. Eng. June 2016; 138(3): 031101. https://doi.org/10.1115/1.4032892
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