Design of a robust neural network structure for determining initial stability particulars of fishing vessels

Alkan A. D., Gulez K., Yilmaz H.

OCEAN ENGINEERING, cilt.31, ss.761-777, 2004 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1016/j.oceaneng.2003.08.002
  • Dergi Adı: OCEAN ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.761-777
  • Anahtar Kelimeler: initial stability, ship parameters, center of gravity, metacenter, neural network
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Stability problem is a vital issue as the total measure of the ship safety. Designers need to use reliable design tools for the definition of stability parameters during the preliminary design stage of ships. These tools are mostly built in the form of approximate expressions with some error level. In this study, a functional and reliable tool is proposed to ship designers for determining initial stability particulars of fishing vessels. It uses a robust neural network (NN) structure with different algorithms based on two fishing vessel databases containing the hull geometry and stability related parameters. The initial stability particulars of fishing vessels are almost exactly determined for an input set of ship data. With this method, using some sample ship data, the vertical center of gravity (KG), height of transverse metacenter above keel (KM) and vertical center of buoyancy (KB) are easily calculated. As a result, the designer can calculate transverse metacentric height (GM) and investigate a possible set of ship parameters affecting the ship's intact stability. (C) 2003 Published by Elsevier Ltd.