Comparative Evaluation Of Nonlinear Identification Approaches

Märzinger Christian, Andreas Schrempf, Luigi Del Re, Grünbacher Engelbert

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review


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).
Original languageEnglish
Title of host publicationProceedings of the Vienna Conference on Mathematical Modelling
Number of pages10
Publication statusPublished - 2015
Event8th Vienna Conference on Mathematical Modelling - Wien, Austria
Duration: 18 Feb 201520 Feb 2015


Conference8th Vienna Conference on Mathematical Modelling
Internet address


  • subspace identification
  • state-affine systems


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