Home Journals Articles Updates About
CN
image
收藏切换
A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine
收藏切换
Tian–qi PEI1, Cao–yang YU1, Lian LIAN1, 2
Journal of Ship Mechanics | 2025, 29(12) : 1838 - 1847
Less
收藏切换
Journal of Ship Mechanics | 2025, 29(12): 1838-1847
• Hydrodynamics •
A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine
Full
Tian–qi PEI1, Cao–yang YU1, Lian LIAN1, 2
Affiliations
  • 1.School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China
  • 2.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Published: 2025-12-15 doi: 10.3969/j.issn.1007-7294.2025.12.002
Outline
收藏切换

Accurate motion prediction is crucial for the safe navigation of Autonomous Underwater Vehicles (AUVs). A fast Least Squares-Support Vector Machine (LS-SVM) motion prediction approach based on ridge regression algorithm is proposed in this paper. Firstly, the ridge regression analysis was incorporated into the traditional hydrodynamic model and the correlation analysis was conducted on the acceleration time-series input variables to identify the relatively important components. This step effectively reduces computational complexity while maintaining prediction accuracy. Subsequently, for the issue of high dimensionality and computational complexity in the LS-SVM algorithm's kernel function matrix, an improved Lagrange function was designed to eliminate redundant bias terms. This modification lightens the burden of calculating high-dimensional kernel matrices and further enhances the speed of maneuvering prediction. Finally, case studies based on the REMUS model demonstrate that the proposed strategy, compared to the standard LS-SVM prediction method that relies on traditional hydrodynamic models, reduces computational runtime by 29.8% while ensuring prediction accuracy.

Autonomous Underwater Vehicle  /  motion prediction  /  Support Vector Machine  /  ridge regression algorithm
裴天琪, 于曹阳, 连琏. 基于岭回归约简和改进支持向量机的AUV运动快速预测方法. 船舶力学, 2025 , 29 (12) : 1838 -1847 . DOI: 10.3969/j.issn.1007-7294.2025.12.002
Tian–qi PEI, Cao–yang YU, Lian LIAN. A fast motion prediction method for AUVs based on ridge regression reduction and improved Support Vector Machine[J]. Journal of Ship Mechanics, 2025 , 29 (12) : 1838 -1847 . DOI: 10.3969/j.issn.1007-7294.2025.12.002
Appendix
Less
Year 2025 volume 29 Issue 12
623
329
Cite this Article
BibTeX
Article Info
doi: 10.3969/j.issn.1007-7294.2025.12.002
  • Receive Date：2024-07-17
  • Online Date：2026-07-07
  • Published：2025-12-15
Article Data
Affiliations
History
  • Received：2024-07-17
Affiliations
    1.School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China
    2.State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
References
Share
https://castjournals.cast.org.cn/joweb/cblx/EN/10.3969/j.issn.1007-7294.2025.12.002
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House