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Comparison of Different Neural Networks Performances on Motorboat Datasets

  • M. Fatih Amasyalı
  • Mert Bal
  • Uğur B. Çelebi
  • Serkan Ekinci
  • U. Kaşif Boyacı
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)

Abstract

Calculation of the required engine power and displacement takes an important place in the initial design of motorboats. Recently, several calculation methods with fewer parameters and with a possible gain of time compared to classical methods have been proposed. This study introduces a novel calculation method based on neural networks. The method requires less data input and hence is more easily applicable than classical methods. In this study several different neural network methods have been conducted on data sets which have principal parameters of motorboats and the respective performances have been presented. From the results obtained, displacement and engine power prediction for motor boats can be used at a suitable level for ship building industry.

Keywords

Hide Layer Radial Basis Function Engine Power Radial Basis Function Neural Network Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Fatih Amasyalı
    • 1
  • Mert Bal
    • 2
  • Uğur B. Çelebi
    • 3
  • Serkan Ekinci
    • 3
  • U. Kaşif Boyacı
    • 4
  1. 1.Computer Engineering DepartmentYıldız Technical UniversityİstanbulTurkey
  2. 2.Mathematical Engineering DepartmentYıldız Technical UniversityİstanbulTurkey
  3. 3.Naval Architecture DepartmentYıldız Technical UniversityİstanbulTurkey
  4. 4.TÜBİTAK, UEKAENational Research Institute of Electronics and CryptologyGebze, KocaeliTurkey

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