Academic Editor: Mamdouh M. El Kady
Copyright © 2013 David Moreno-Salinas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of
the experimental systems is of key importance. In marine systems, the changing environmental conditions and the
complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate
simulations. There exist a wide number of techniques to define mathematical models from experimental data.
Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research
areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability
is demonstrated through the identification of the Nomoto second-order ship model with real experimental data
obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using
Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway
speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model
obtained is validated through experimental tests that illustrate the potential of SVM for system identification.