This paper describes a parametric identification tool for predicting the hydrodynamic forces acting on a submarine model using its motion history. The tool uses a neural network to identify the hydrodynamic forces and moments; the network was trained with data obtained from multi-degree-of-freedom captive maneuvering tests. The characteristics of the trained network are demonstrated through reconstruction of the force and moment time histories. This technique has the potential to reduce experimental time and cost by enabling a full hydrodynamic model of the vehicle to be obtained from a relatively limited number of test maneuvers.

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