Author
Johan Schoukens
Other affiliations: Eindhoven University of Technology, VU University Amsterdam
Bio: Johan Schoukens is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Nonlinear system & Linear approximation. The author has an hindex of 20, co-authored 76 publications receiving 1860 citations. Previous affiliations of Johan Schoukens include Eindhoven University of Technology & VU University Amsterdam.
Papers published on a yearly basis
Papers
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TL;DR: This paper gives a survey of frequency domain identification methods for rational transfer functions in the Laplace (s) or z-domain through a study of the (equivalent) cost functions.
Abstract: This paper gives a survey of frequency domain identification methods for rational transfer functions in the Laplace (s) or z-domain. The interrelations between the different approaches are highlighted through a study of the (equivalent) cost functions. The properties of the various estimators are discussed and illustrated by several examples. >
508 citations
TL;DR: In this article, the impact of nonlinear distortions on linear system identification was studied and a theoretical framework was proposed that extends the linear system description to include nonlinear distortion: the nonlinear system is replaced by a linear model plus a nonlinear noise source.
181 citations
27 Aug 2003
TL;DR: A theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'.
Abstract: This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.
119 citations
TL;DR: In this paper, the impact of nonlinear distortions on the linear system identification framework is studied and a fast approximate nonlinear modelling framework is set up that is a natural extension of the linear framework, and bridges the gap between the linear and the nonlinear identification approaches.
88 citations
17 Jul 2013
TL;DR: Three sets of data suitable for development, testing and benchmarking of system identification algorithms for nonlinear systems are presented, collected from laboratory processes that can be described by block - oriented dynamic models, and by more general nonlinear difference and differential equation models.
Abstract: System identification is a fundamentally experimental field of science in that it deals with modeling of system dynamics using measured data. Despite this fact many algorithms and theoretical results are only tested with simulations at the time of publication. One reason for this may be a lack of easily available live data. This paper therefore presents three sets of data, suitable for development, testing and benchmarking of system identification algorithms for nonlinear systems. The data sets are collected from laboratory processes that can be described by block - oriented dynamic models, and by more general nonlinear difference and differential equation models. All data sets are available for free download.
83 citations
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11 Dec 2012
1,704 citations
TL;DR: In this article, a review of stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions is presented. But it is not shown that many of these methods have an output-only counterpart.
Abstract: This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods havean output-only counterpart. For instance, the Complex Mode Indication Function (CMIF) can be applied both to Frequency Response Functions and output power and cross spectra. The Polyreference Time Domain (PTD) method applied to impulse responses is similar to the Instrumental Variable (IV) method applied to output covariances. The Eigensystem Realization Algorithm (ERA) is equivalent to stochastic subspace identification.
849 citations