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Neural networks for system identification of coupled ship dynamics

Martin, P. and Katebi, Reza and Yamamoto, I. and Daigo, K. and Kobayashi, E. and Matsuura, M. and Hashimoto, M. and Hirayama, H. and Okamoto, N. (2002) Neural networks for system identification of coupled ship dynamics. [Proceedings Paper]

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Abstract

System identification of coupled ship dynamics is problematic with standard least squares methods due to the non-linear, multivariable nature of the system. Neural Networks have therefore been applied to this problem, as they are particularly suitable for approximating non-linear, multivariable functions. In this paper, results of identification with Neural Networks are given for a ship motion simulation based on a standard mathematical model, and for real data collected from a 1/50(th) scale model of the system. The method is seen to be successful at various operating points, and ideas for extension of the work are discussed.

Item type: Proceedings Paper
ID code: 39573
Keywords: neural networks, system identification, coupled ship dynamics, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 07 May 2012 14:48
    Last modified: 17 Jul 2013 14:06
    URI: http://strathprints.strath.ac.uk/id/eprint/39573

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