Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction
In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the proc...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/8/1572 |
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| author | Yao Meng Xianku Zhang Guoqing Zhang Xiufeng Zhang Yating Duan |
| author_facet | Yao Meng Xianku Zhang Guoqing Zhang Xiufeng Zhang Yating Duan |
| author_sort | Yao Meng |
| collection | DOAJ |
| description | In order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the processed Sinc function dataset, and (2) the tank test dataset of the KRISO container ship (KCS) model. The KCS was taken as the main research plant, and the motion prediction models of KCS were obtained. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ε</mi></mrow></semantics></math></inline-formula>-support vector regression and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>υ</mi></mrow></semantics></math></inline-formula>-support vector regression were taken as the compared algorithms. The sparsity, effectiveness, and generalization of the three algorithms were analyzed. According to the trained prediction models of the three algorithms, the number of relevance vectors was compared with the number of support vectors. From the prediction results of the Sinc function and tank test datasets, the highest percentage of relevance vectors in the trained sample was below 17%. The final prediction results indicated that the proposed nonparametric models had good prediction performance. They could ensure good sparsity while ensuring high prediction accuracy. Compared with the SVR, the prediction accuracy can be improved by more than 14.04%, and the time consumption was also relatively lower. A training model with good sparsity can reduce prediction time. This is essential for the online prediction of ship motion. |
| first_indexed | 2024-03-10T23:49:18Z |
| format | Article |
| id | doaj.art-38446fa5385441e49335d6d21d553a7f |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T23:49:18Z |
| publishDate | 2023-08-01 |
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| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-38446fa5385441e49335d6d21d553a7f2023-11-19T01:46:00ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-08-01118157210.3390/jmse11081572Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion PredictionYao Meng0Xianku Zhang1Guoqing Zhang2Xiufeng Zhang3Yating Duan4Navigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaNavigation College, Dalian Maritime University, Dalian 116026, ChinaIn order to establish a sparse and accurate ship motion prediction model, a novel Bayesian probability prediction model based on relevance vector machine (RVM) was proposed for nonparametric modeling. The sparsity, effectiveness, and generalization of RVM were verified from two aspects: (1) the processed Sinc function dataset, and (2) the tank test dataset of the KRISO container ship (KCS) model. The KCS was taken as the main research plant, and the motion prediction models of KCS were obtained. The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>ε</mi></mrow></semantics></math></inline-formula>-support vector regression and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>υ</mi></mrow></semantics></math></inline-formula>-support vector regression were taken as the compared algorithms. The sparsity, effectiveness, and generalization of the three algorithms were analyzed. According to the trained prediction models of the three algorithms, the number of relevance vectors was compared with the number of support vectors. From the prediction results of the Sinc function and tank test datasets, the highest percentage of relevance vectors in the trained sample was below 17%. The final prediction results indicated that the proposed nonparametric models had good prediction performance. They could ensure good sparsity while ensuring high prediction accuracy. Compared with the SVR, the prediction accuracy can be improved by more than 14.04%, and the time consumption was also relatively lower. A training model with good sparsity can reduce prediction time. This is essential for the online prediction of ship motion.https://www.mdpi.com/2077-1312/11/8/1572nonparametric identification modelingship motion predictionrelevance vector machinesparsity analysissupport vector regression |
| spellingShingle | Yao Meng Xianku Zhang Guoqing Zhang Xiufeng Zhang Yating Duan Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction Journal of Marine Science and Engineering nonparametric identification modeling ship motion prediction relevance vector machine sparsity analysis support vector regression |
| title | Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction |
| title_full | Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction |
| title_fullStr | Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction |
| title_full_unstemmed | Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction |
| title_short | Sparse Bayesian Relevance Vector Machine Identification Modeling and Its Application to Ship Maneuvering Motion Prediction |
| title_sort | sparse bayesian relevance vector machine identification modeling and its application to ship maneuvering motion prediction |
| topic | nonparametric identification modeling ship motion prediction relevance vector machine sparsity analysis support vector regression |
| url | https://www.mdpi.com/2077-1312/11/8/1572 |
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