PlumX Metrics
Embed PlumX Metrics

Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression

Journal of Marine Science and Engineering, ISSN: 2077-1312, Vol: 11, Issue: 5
2023
  • 9
    Citations
  • 0
    Usage
  • 7
    Captures
  • 2
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    9
  • Captures
    7
  • Mentions
    2
    • Blog Mentions
      1
      • Blog
        1
    • News Mentions
      1
      • News
        1

Most Recent News

Reports Summarize Marine Science and Engineering Research from Wuhan University of Technology (Parameter Prediction of the Non-Linear Nomoto Model for Different Ship Loading Conditions Using Support Vector Regression)

2023 JUN 14 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Fresh data on marine science and engineering are presented

Article Description

Significant changes in the load of cargo ships make it difficult to simulate and control their motion. In this work, a parameter prediction method for a ship maneuvering motion model is developed based on parameter identification and support vector regression (SVR). First, the effects of least-squares (LS) and multi-innovation least-squares (MILS) parameter identification methods for the non-linear Nomoto model are investigated. The MILS method is then used to identify the parameters of the non-linear Nomoto model under various load conditions, and model training datasets are established. On this basis, SVR is used to predict the parameters of the non-linear Nomoto model. The results reveal that the MILS method converges faster than the LS method. The SVR method achieves lower accuracy than the MILS method, but exhibits reasonable prediction accuracy for zigzag motions, and the maneuvering motion model can be predicted as navigation conditions change.

Bibliographic Details

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know