Comparative Evaluation of Nonlinear Identification Approaches
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of 5th MATHOD Conference
Original Kurzfassung:
Design of data based models for nonlinear systems using universal approximators has become a standard issue for which several tool boxes exist. The validation of such models is usually done using data of the same range and distribution as the identification data. This tends to hide the fact that the choice of the basis
function used for the approximation is decisive in terms of model quality, and in particular of its extrapolation
qualities. To this end, this paper compares a subspace identification procedure for a class of nonlinear systems with standard neural networks. As the results shown confirm, the subspace identification procedure in its simple form is not able to yield consistent estimates, but after a suitable robustification proves clearly superior to the ANN both in terms of performance (in terms of VAF) and of complexity (in terms of the number of parameters).
Sprache der Kurzfassung:
Englisch
Erscheinungsjahr:
2006
Anzahl der Seiten:
10
Notiz zur Publikation:
Proceedings of 5th MATHOD Conference, February 8-10, 2006; Vienna, Austria