On-line prediction of ship roll motion during maneuvering using sequential learning RBF neural networks

The on-line prediction of ship roll motion during maneuvering plays an important role in navigation safety and ship control applications. This paper presents an on-line prediction model of ship roll motion via a variable structure radial basis function neural network (RBFNN), whose structure and parameters are tuned in real time based on a sliding data window observer. The RBFNN is sequentially constructed by adding the new sample in the hidden layer and pruning the obsolete hidden units at each epoch, with the connecting parameters adjusted simultaneously. Gaussian functions with multi-scale kernel width are adopted to provide more flexible representations of model input terms and to achieve better generalization capability. Simulation study of ship roll motion prediction is conducted with measurement data of turning circle test and zigzag test in full-scale sea trial. Results demonstrate that the proposed neural network predictive model can on-line predict the roll angle with high accuracy. The predictive model is also featured with its compact network structure and fast computational speed.

• Record URL:
  https://doi.org/10.1016/j.oceaneng.2013.01.005
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0029801813000206
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • Abstract reprinted with permission from Elsevier.
• Authors:
  • Yin, Jian-chuan
  • Zou, Zao-jian
  • Xu, Feng
• Publication Date: 2013-3-15

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 139-147
• Serial:
  • Ocean Engineering
  • Volume: 61
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Maneuvering; Mathematical prediction; Neural networks; Real time data processing; Rolling; Ship motion
• Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment; I91: Vehicle Design and Safety; I92: Vehicle Comfort;

Filing Info

• Accession Number: 01487759
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 4 2013 12:37PM