Picture of Open Access badges

Discover Open Access research at Strathprints

It's International Open Access Week, 24-30 October 2016. This year's theme is "Open in Action" and is all about taking meaningful steps towards opening up research and scholarship. The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Explore recent world leading Open Access research content by University of Strathclyde researchers and see how Strathclyde researchers are committing to putting "Open in Action".


Image: h_pampel, CC-BY

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. In: Control applications in marine systems 2001 (CAMS 2001). IFAC Proceedings Series . PERGAMON-ELSEVIER SCIENCE LTD, Kidlington, pp. 83-88. ISBN 0080432360

Full text not available in this repository. (Request a copy from the Strathclyde author)


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.