J
Johan Schoukens
Researcher at Vrije Universiteit Brussel
Publications - 76
Citations - 2053
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
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Journal ArticleDOI
Parametric identification of transfer functions in the frequency domain-a survey
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.
Journal ArticleDOI
Identification of linear systems with nonlinear distortions
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.
Identification of Linear Systems with Nonlinear Distortions
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'.
Journal ArticleDOI
Identification of Linear Systems with Nonlinear Distortions
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.
Proceedings ArticleDOI
Three free data sets for development and benchmarking in nonlinear system identification
Torbjörn Wigren,Johan Schoukens +1 more
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.