14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThA5.6

Kollár, István (Budapest Univ. of Tech. and Econ.), Pintelon, Rik (Vrije Univ. Brussel), Schoukens, Johan (Vrije Univ. Brussel)

Frequency Domain System Identification Toolbox for Matlab: Characterizing Nonlinear Errors of Linear Models

Scheduled for presentation during the Poster Session "Software Demonstration Session I" (ThA5), Thursday, March 30, 2006, 10:30−12:30, Mulubinba Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Nonlinear System Identification, Frequency Domain Identification, Toolboxes


System identification often denotes the determination of linear models from input-output data. The behaviour of many systems can be described by an s-domain or z-domain transfer function model, at least for a given excitation amplitude range. The quality of the fit can be assessed by the analysis of the residuals, that is, of the difference between the measured data and the model.

However, even slight nonlinearities can be misleading, by causing part of the residuals non-explicable by the linear model. We cannot simply tell if the excess residual error is due to undermodelling or to nonlinear system behaviour. This can lead to erroneous overmodelling. Therefore, characterisation of the nonlinear system behaviour is essential in the verification of linear models.

The Frequency Domain System Identification Toolbox has been extended with analysis tools of nonlinear system behaviour. Specially designed excitation signals allow the description of nonlinearity levels. By this, model verification becomes possible even if nonlinear error terms excess linear additive noise.