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APPLICATION OF AN ARTIFICIAL NEURAL NETWORK AND MULTIPLE NONLINEAR REGRESSION TO ESTIMATE CONTAINER SHIP LENGTH BETWEEN PERPENDICULARS

Abstract

Container ship length was estimated using artificial neural networks (ANN), as well as a random search based on Multiple Nonlinear Regression (MNLR). Two alternative equations were developed to estimate the length between perpendiculars based on container number and ship velocity using the aforementioned methods and an up-to-date container ship database. These equations could have practical applications during the preliminary design stage of a container ship. The application of heuristic techniques for the development of a MNLR model by variable and function randomisation leads to the automatic discovery of equation sets. It has been shown that an equation elaborated using this method, based on a random search, is more accurate and has a simpler mathematical form than an equation derived using ANN.

Keywords:

ship design, ANN, regression, container ship, length

Details

Issue
Vol. 28 No. 2(110) (2021)
Section
Latest Articles
Published
15-07-2021
DOI:
https://doi.org/10.2478/pomr-2021-0019
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Authors

  • Tomasz Cepowski

    Akademia Morska w Szczecinie
  • Paweł Chorab

    Akademia Morska w Szczecinie
  • Dorota Łozowicka

    Akademia Morska w Szczecinie

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