A NEW METHOD OF SHIP WEATHER ROUTING USING NEURAL NETWORK

This paper describes a new approach to the strategic weather routeing problem using a neural network which is known as a powerful tool for pattern recognition. The 5-day mean 500Pa heights over the North Pacific Ocean for the first 5 days and the latter 5 days during a voyage were input to a neural network. The teacher signals were prepared by simulating the navigation of a container ship on various routes from San Francisco to Tokyo using the analyzed wave data and calculating the passage times of these routes. Learning of a neural network was performed for 105 voyages in 5 winter seasons. To verify the generalization ability, a new set of 5-day mean 500Pa heights in different winter seasons were input to a trained neural network. As a results, a trained neural network could provide the optimum or sub-optimum routes for most of the voyages.

• Supplemental Notes:
  • MARIND '96, 1st Intl Conf on Marine Industry; 2-7 June 1996, Varna, Bulgaria. Organised by Bulgarian Soc Naval Architects & Marine Engrs et al. Ed by P.A. Bogdanov. Procs. Vol II, p 243 [9 p, 4 ref, 9 fig]
• Authors:
  • HAGIWARA, H
  • Shoji, R
  • Sugisaki, A M
• Publication Date: 1996

Language

• English

Subject/Index Terms

• TRT Terms: Neural networks
• Uncontrolled Terms: Ship routing
• Subject Areas: Data and Information Technology; Marine Transportation;

Filing Info

• Accession Number: 00733460
• Record Type: Publication
• Source Agency: British Maritime Technology
• Files: TRIS
• Created Date: Mar 27 1997 12:00AM