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Grey-box modeling of ship maneuvering motion based on AE-LS-SVM
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Qiang Yuan1, *, Zhihong Liu1, Jinzhi Peng1, Yujie Jiang1, Fei Dong1, Ruiping Zhou2
Navigation of China | 2026, 49(4) : 12 - 20
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Navigation of China | 2026, 49(4): 12-20
• Intelligent Shipping •
Grey-box modeling of ship maneuvering motion based on AE-LS-SVM
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Qiang Yuan1, *, Zhihong Liu1, Jinzhi Peng1, Yujie Jiang1, Fei Dong1, Ruiping Zhou2
Affiliations
  • 1.School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China
  • 2.School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
Published: 2026-08-25 doi: 10.3969/j.issn.1000-4653.2026.04.002
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This study aims to improve the accuracy of ship motion prediction by proposing a gray-box modeling method for ship maneuvering motion, termed AE-LS-SVM, which combines the Alpha Evolution (AE) algorithm to optimize the Least Squares Support Vector Machine (LS-SVM). The AE algorithm's superior global search capability was employed to optimize the hyperparameters of LS-SVM. The dataset in this study was obtained through ship maneuvering tests conducted using the Marine Systems Simulator (MSS). Based on the Abkowitz model, a three-Degree-of-Freedom (3-DOF) gray-box model of ship maneuvering motion was constructed, incorporating maneuvering variables and ship responses. The gray-box model established in this study was then subjected to comparative analysis with gray-box models developed using GWO-LS-SVM (Grey Wolf Optimizer) and PSO-LS-SVM (Particle Swarm Optimization) through maneuvering tests. Experimental results demonstrate that the AE-LS-SVM method achieves reductions in Mean Squared Error (MSE) of approximately 6% and 4%, reductions in Mean Absolute Percentage Error (MAPE) of 3.84% and 1.96%, and improvements in correlation coefficient (CC) of 0.13% and 0.04%, respectively, compared to the two benchmark methods. Additionally, the anti-interference capability of the proposed method was validated by introducing varying levels of noise into the training dataset to simulate measurement errors. The findings indicate that the AE-LS-SVM method exhibits high modeling accuracy and robust noise resistance, offering valuable support for ship motion prediction.

intelligent shipping  /  ship maneuvering motion  /  system identification  /  least square support vector machine  /  alpha evolution  /  grey-box modeling
袁强, 刘志鸿, 彭金芝, 姜裕杰, 董飞, 周瑞平. 基于AE-LS-SVM的船舶操纵运动灰箱建模方法. 中国航海, 2026 , 49 (4) : 12 -20 . DOI: 10.3969/j.issn.1000-4653.2026.04.002
Qiang Yuan, Zhihong Liu, Jinzhi Peng, Yujie Jiang, Fei Dong, Ruiping Zhou. Grey-box modeling of ship maneuvering motion based on AE-LS-SVM[J]. Navigation of China, 2026 , 49 (4) : 12 -20 . DOI: 10.3969/j.issn.1000-4653.2026.04.002
Year 2026 volume 49 Issue 4
128
32
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Article Info
doi: 10.3969/j.issn.1000-4653.2026.04.002
  • Receive Date:2025-09-12
  • Online Date:2026-09-28
  • Published:2026-08-25
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  • Received:2025-09-12
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Affiliations
    1.School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022, China
    2.School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
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Percentage of
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Genus
种数
Number of
species
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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