Combined with the free-running model tests of KVLCC ship, the system identification (SI) based on support vector machines (SVM) is proposed for the prediction of ship maneuvering motion. The hydrodynamic derivatives in an Abkowitz model are determined by the Lagrangian factors and the support vectors in the SVM regression model. To obtain the optimized structural factors in SVM, particle swarm optimization (PSO) is incorporated into SVM. To diminish the drift of hydrodynamic derivatives after regression, a difference method is adopted to reconstruct the training samples before identification. The validity of the difference method is verified by correlation analysis. Based on the Abkowitz mathematical model, the simulation of ship maneuvering motion is conducted. Comparison between the predicted results and the test results demonstrates the validity of the proposed methods in this paper.
Support Vector Machines (SVM) based system identification is applied to predict ship maneuvering motion. Different from the prediction methods based on the explicit mathematical model of ship maneuvering motion, the black-box model of ship maneuvering motion is constructed and used to predict ship maneuvering motion. With the rudder angle and the variables of maneuvering motion as inputs and the hydrodynamic forces as outputs, the complicated nonlinear functions in the Abkowitz model are identified; and the surge force, sway force and yaw moment are predicted blindly by using the functions identified. Taking turning test as example, with the rudder angle as inputs and the maneuverability parameters of turning circles as outputs, the input-output mappings are identified and the maneuverability parameters such as the advance, the transfer and the tactical diameter are also predicted blindly by using the identified mappings.
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