EFFICIENT PARAMETERIZATION FOR GREY-BOX MODEL IDENTIFICATION OF COMPLEX PHYSICAL SYSTEMS

Grey box model identification preserves known physical structures in a model but with limits to the possible excitation, all parameters are rarely identifiable, and different parametrizations give significantly different model quality. Convenient methods to show which parameterizations are the better would be very useful. This paper shows how we can assess the parameter interdependence and model quality. Hessian matrix decomposition is employed to show linear dependencies between variables and to put a quality tag on different parameterizations. The method determines parameter relations that need be constrained to achieve satisfactory convergence. Identification of nonlinear models for a ship illustrate the concept.