Theory and application of artificial neural networks for the real time prediction of ship motion

  • Authors:

  • Ameer Khan;Cees Bil;Kaye E. Marion

  • Affiliations:

  • School of Aerospace, Manufacturing and Mechanical Engineering, RMIT University, Melbourne, Victoria, Australia;School of Aerospace, Manufacturing and Mechanical Engineering, RMIT University, Melbourne, Victoria, Australia;School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria, Australia

  • Venue:
  • KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
  • Year:
  • 2005

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Abstract

Due to the random nature of the ship's motion in an open water environment, the deployment and the landing of vehicles from a ship can often be difficult and even dangerous. The ability to predict reliably the motion will allow improvements in safety on board ships and facilitate more accurate deployment of vehicles off ships. This paper presents an investigation into the application of artificial neural network methods for the prediction of ship motion. Two training techniques for the determination of the artificial neural network weights are presented. It is shown that the artificial neural network based on the singular value decomposition produces excellent predictions and is able to predict the ship motion in real time for up to 10 seconds.