Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes
Dynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, espe...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2024.2341922 |
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| _version_ | 1850111714877505536 |
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| author | Yaohui Yu Hongbin Hao Zihao Wang Yan Peng Shaorong Xie |
| author_facet | Yaohui Yu Hongbin Hao Zihao Wang Yan Peng Shaorong Xie |
| author_sort | Yaohui Yu |
| collection | DOAJ |
| description | Dynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, especially for reoccurring changes. The method contains two main mechanisms: the change monitoring mechanism and the adaptive weighting mechanism. The former identifies the change in ship dynamics and decides when to incorporate a new base model; the latter adjusts the weights of the base models to align with current scenarios, thus ensuring the predictive accuracy. To assess the method’s effectiveness under varying ship dynamics, the online prediction of ship maneuvering motions under speed-induced dynamic changes is investigated. Compared with the offline model, the result demonstrates the superiority of the adaptive weighted ensemble model. The proposed method can consistently provide accurate predictions in the scenarios with reoccurring changes, and can also enhance the model capability by adjusting weights to cope with some unencountered changes. |
| format | Article |
| id | doaj-art-e0d13e4988694fd888310050484305ce |
| institution | OA Journals |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-e0d13e4988694fd888310050484305ce2025-08-20T02:37:33ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2024-12-0118110.1080/19942060.2024.2341922Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changesYaohui Yu0Hongbin Hao1Zihao Wang2Yan Peng3Shaorong Xie4School of Artificial Intelligence, Shanghai University, Shanghai, People’s Republic of ChinaDepartment of Civil and Environment Engineering, The Hong Kong Ploytechnic University, Kowloon, Hong Kong Special Administrative Region, Hong Kong, People’s Republic of ChinaSchool of Artificial Intelligence, Shanghai University, Shanghai, People’s Republic of ChinaSchool of Artificial Intelligence, Shanghai University, Shanghai, People’s Republic of ChinaEngineering Research Center of Unmanned Intelligent Marine Equipment, Ministry of Education, Shanghai, People’s Republic of ChinaDynamic changes in ship maneuverability challenge the accuracy and effectiveness of ship maneuvering models. This paper proposes an online prediction method based on the adaptive weighted ensemble learning framework, which can adaptively update the model according to changes in maneuverability, especially for reoccurring changes. The method contains two main mechanisms: the change monitoring mechanism and the adaptive weighting mechanism. The former identifies the change in ship dynamics and decides when to incorporate a new base model; the latter adjusts the weights of the base models to align with current scenarios, thus ensuring the predictive accuracy. To assess the method’s effectiveness under varying ship dynamics, the online prediction of ship maneuvering motions under speed-induced dynamic changes is investigated. Compared with the offline model, the result demonstrates the superiority of the adaptive weighted ensemble model. The proposed method can consistently provide accurate predictions in the scenarios with reoccurring changes, and can also enhance the model capability by adjusting weights to cope with some unencountered changes.https://www.tandfonline.com/doi/10.1080/19942060.2024.2341922Online predictionensemble learningconcept driftnon-stationary environmentsautonomous shipship maneuvering motion |
| spellingShingle | Yaohui Yu Hongbin Hao Zihao Wang Yan Peng Shaorong Xie Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes Engineering Applications of Computational Fluid Mechanics Online prediction ensemble learning concept drift non-stationary environments autonomous ship ship maneuvering motion |
| title | Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| title_full | Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| title_fullStr | Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| title_full_unstemmed | Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| title_short | Online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| title_sort | online prediction of ship maneuvering motions based on adaptive weighted ensemble learning under dynamic changes |
| topic | Online prediction ensemble learning concept drift non-stationary environments autonomous ship ship maneuvering motion |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2024.2341922 |
| work_keys_str_mv | AT yaohuiyu onlinepredictionofshipmaneuveringmotionsbasedonadaptiveweightedensemblelearningunderdynamicchanges AT hongbinhao onlinepredictionofshipmaneuveringmotionsbasedonadaptiveweightedensemblelearningunderdynamicchanges AT zihaowang onlinepredictionofshipmaneuveringmotionsbasedonadaptiveweightedensemblelearningunderdynamicchanges AT yanpeng onlinepredictionofshipmaneuveringmotionsbasedonadaptiveweightedensemblelearningunderdynamicchanges AT shaorongxie onlinepredictionofshipmaneuveringmotionsbasedonadaptiveweightedensemblelearningunderdynamicchanges |