Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set
Artificial neural networks are applied to model the manoeuvrability characteristics of a ship based on empirical information acquired from experiments with a scaled model. This work aims to evaluate the performance of the proposed method of training the artificial neural network model even with a ve...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/1/15 |
| _version_ | 1827624370514690048 |
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| author | Lúcia Moreira C. Guedes Soares |
| author_facet | Lúcia Moreira C. Guedes Soares |
| author_sort | Lúcia Moreira |
| collection | DOAJ |
| description | Artificial neural networks are applied to model the manoeuvrability characteristics of a ship based on empirical information acquired from experiments with a scaled model. This work aims to evaluate the performance of the proposed method of training the artificial neural network model even with a very small quantity of noisy data. The data used for the training consisted of zig-zag and circle manoeuvres carried out in agreement with the IMO standards. The wind effect is evident in some of the recorded experiments, creating additional disturbance to the fitting scheme. The method used for the training of the network is the Levenberg–Marquardt algorithm, and the results are compared with the scaled conjugate gradient method and the Bayesian regularization. The results obtained with the different methodologies show very suitable accuracy in the prediction of the referred manoeuvres. |
| first_indexed | 2024-03-09T12:07:40Z |
| format | Article |
| id | doaj.art-6f62dc14e3064656aba317740d89ade2 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T12:07:40Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-6f62dc14e3064656aba317740d89ade22023-11-30T22:56:00ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-12-011111510.3390/jmse11010015Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data SetLúcia Moreira0C. Guedes Soares1Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalCentre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, PortugalArtificial neural networks are applied to model the manoeuvrability characteristics of a ship based on empirical information acquired from experiments with a scaled model. This work aims to evaluate the performance of the proposed method of training the artificial neural network model even with a very small quantity of noisy data. The data used for the training consisted of zig-zag and circle manoeuvres carried out in agreement with the IMO standards. The wind effect is evident in some of the recorded experiments, creating additional disturbance to the fitting scheme. The method used for the training of the network is the Levenberg–Marquardt algorithm, and the results are compared with the scaled conjugate gradient method and the Bayesian regularization. The results obtained with the different methodologies show very suitable accuracy in the prediction of the referred manoeuvres.https://www.mdpi.com/2077-1312/11/1/15ship’s manoeuvrabilitymodel tests dataartificial neural networks |
| spellingShingle | Lúcia Moreira C. Guedes Soares Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set Journal of Marine Science and Engineering ship’s manoeuvrability model tests data artificial neural networks |
| title | Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set |
| title_full | Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set |
| title_fullStr | Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set |
| title_full_unstemmed | Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set |
| title_short | Simulating Ship Manoeuvrability with Artificial Neural Networks Trained by a Short Noisy Data Set |
| title_sort | simulating ship manoeuvrability with artificial neural networks trained by a short noisy data set |
| topic | ship’s manoeuvrability model tests data artificial neural networks |
| url | https://www.mdpi.com/2077-1312/11/1/15 |
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