Blind Prediction of Ship Maneuvering by Using Support Vector Machines

Abstract

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.