@inproceedings{4bf9ddea9ffb4d3eacea3f6057b4faba,

title = "Neural networks for system identification of coupled ship dynamics",

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. ",

keywords = "neural networks, system identification, coupled ship dynamics",

author = "P. Martin and Reza Katebi and I. Yamamoto and K. Daigo and E. Kobayashi and M. Matsuura and M. Hashimoto and H. Hirayama and N. Okamoto",

year = "2002",

language = "English",

isbn = "0080432360 ",

series = "IFAC Proceedings Series",

publisher = "Pergamon-Elsevier",

pages = "83--88",

editor = "R. Katebi",

booktitle = "Control applications in marine systems 2001 (CAMS 2001)",

note = "IFAC Conference on Control Applications in Marine Systems ; Conference date: 18-07-2001 Through 20-07-2001",

}