Wang, Zi., Guedes Soares, C. and Zou, Z.J. (2019), “Investigation of Training Data Selection in the Black-box Modeling of Ship Maneuvering Motion”, 11th International Workshop on Ship and Marine Hydrodynamics (IWSH 2019), 22-25 September, Hamburg, Germany

For the identification modeling of ship maneuvering motion, a variety of training data are applied and compared to select appropriate excitation signal with more dynamic information, hence ensuring the generalization ability of the identified model. The identification framework is black-box modeling based on the v ("nu") -support vector machine algorithm with radial basis function kernel, which automatically controls the number of support vectors and keeps sparsity. A Mariner class ship is taken as the study object, and the training data is generated from the reliable mathematical model, including 10o/10o, 20o/20o, 30o/30o zigzag maneuvers and 35º turning circle maneuver. The generalization performance of the identified model under different training data is compared by the predictions of other standard zigzag and turning maneuvers. The results indicate that the 20o/20o and 30o/30o zigzag maneuvers contain more dynamic information and can be used to train the model when the data is pure. The present work provides guidance for the subsequent experiment research to update the ship model quickly in the field.

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