Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for a Nonlinear Ship Maneuvering System
This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/08905487
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Supplemental Notes:
- Copyright © 2018, Chinese Ocean Engineering Society and Springer-Verlag Berlin Heidelberg.
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Authors:
- Bai, Wei-wei
- Ren, Jun-sheng
- Li, Tie-shan
- Publication Date: 2018-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 288-300
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Serial:
- China Ocean Engineering
- Volume: 32
- Issue Number: 3
- Publisher: Chinese Ocean Engineering Society
- ISSN: 0890-5487
- EISSN: 2191-8945
- Serial URL: http://link.springer.com/journal/13344
Subject/Index Terms
- TRT Terms: Iterative methods; Maneuvering; Mathematical models
- Subject Areas: Marine Transportation; Operations and Traffic Management;
Filing Info
- Accession Number: 01675589
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 20 2018 9:18AM