Identification of a Surface Marine Vessel Using LS-SVM
The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/803548 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849412933387288576 |
|---|---|
| author | David Moreno-Salinas Dictino Chaos Jesús Manuel de la Cruz Joaquín Aranda |
| author_facet | David Moreno-Salinas Dictino Chaos Jesús Manuel de la Cruz Joaquín Aranda |
| author_sort | David Moreno-Salinas |
| collection | DOAJ |
| description | The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of
the experimental systems is of key importance. In marine systems, the changing environmental conditions and the
complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate
simulations. There exist a wide number of techniques to define mathematical models from experimental data.
Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research
areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability
is demonstrated through the identification of the Nomoto second-order ship model with real experimental data
obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using
Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway
speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model
obtained is validated through experimental tests that illustrate the potential of SVM for system identification. |
| format | Article |
| id | doaj-art-65880a1ab30f4f69b8a6bbc92f9df0da |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-65880a1ab30f4f69b8a6bbc92f9df0da2025-08-20T03:34:17ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/803548803548Identification of a Surface Marine Vessel Using LS-SVMDavid Moreno-Salinas0Dictino Chaos1Jesús Manuel de la Cruz2Joaquín Aranda3Department of Computer Science and Automatic Control, UNED, Madrid 28040, SpainDepartment of Computer Science and Automatic Control, UNED, Madrid 28040, SpainDepartment of Computer Architecture and Automatic Control, Complutense University of Madrid (UCM), Madrid 28040, SpainDepartment of Computer Science and Automatic Control, UNED, Madrid 28040, SpainThe availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to define mathematical models from experimental data. Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability is demonstrated through the identification of the Nomoto second-order ship model with real experimental data obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model obtained is validated through experimental tests that illustrate the potential of SVM for system identification.http://dx.doi.org/10.1155/2013/803548 |
| spellingShingle | David Moreno-Salinas Dictino Chaos Jesús Manuel de la Cruz Joaquín Aranda Identification of a Surface Marine Vessel Using LS-SVM Journal of Applied Mathematics |
| title | Identification of a Surface Marine Vessel Using LS-SVM |
| title_full | Identification of a Surface Marine Vessel Using LS-SVM |
| title_fullStr | Identification of a Surface Marine Vessel Using LS-SVM |
| title_full_unstemmed | Identification of a Surface Marine Vessel Using LS-SVM |
| title_short | Identification of a Surface Marine Vessel Using LS-SVM |
| title_sort | identification of a surface marine vessel using ls svm |
| url | http://dx.doi.org/10.1155/2013/803548 |
| work_keys_str_mv | AT davidmorenosalinas identificationofasurfacemarinevesselusinglssvm AT dictinochaos identificationofasurfacemarinevesselusinglssvm AT jesusmanueldelacruz identificationofasurfacemarinevesselusinglssvm AT joaquinaranda identificationofasurfacemarinevesselusinglssvm |