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.; Katebi, R., ed. (2002) Neural networks for system identification of coupled ship dynamics. In: Control applications in marine systems 2001 (CAMS 2001). IFAC Proceedings Series . PERGAMON-ELSEVIER SCIENCE LTD, GBR, pp. 83-88. ISBN 0080432360

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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.

ORCID iDs

Martin, P., Katebi, Reza ORCID logoORCID: https://orcid.org/0000-0003-2729-0688, Yamamoto, I., Daigo, K., Kobayashi, E., Matsuura, M., Hashimoto, M., Hirayama, H. and Okamoto, N.; Katebi, R.
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