J
Johan Schoukens
Researcher at Eindhoven University of Technology
Publications - 33
Citations - 1413
Johan Schoukens is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Nonlinear system & Nonlinear system identification. The author has an hindex of 13, co-authored 33 publications receiving 1160 citations. Previous affiliations of Johan Schoukens include Vrije Universiteit Brussel & Pennsylvania State University.
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Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach
TL;DR: A related linear dynamic system (RLDS) approximation to the nonlinear system (NLS) is defined, and it is shown that the differences between the NLS and the RLDS can be modeled as stochastic variables with known properties.
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Identification of nonlinear systems using Polynomial Nonlinear State Space models
TL;DR: A method to model nonlinear systems using polynomial nonlinear state space equations by identifying first the best linear approximation of the system under test is proposed.
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Nonlinear System Identification: A User-Oriented Road Map
Johan Schoukens,Lennart Ljung +1 more
TL;DR: The selection of topics and the organization of the discussion are strongly colored by the personal journey of the authors in this nonlinear universe.
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Survey of excitation signals for FFT based signal analyzers
TL;DR: The properties of ten different excitation signals are studied to analyze their suitability as excitation signal for fast Fourier transform (FFT)-based signal and network analyzers and the flexibility to create a customized amplitude spectrum is investigated.
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Frequency-domain system identification using non-parametric noise models estimated from a small number of data sets
TL;DR: This paper discusses the problem of identifying a linear system from the frequency data when the measurements of the input and the output signals are both disturbed with noise, and shows that the exact covariance matrices can be replaced by the sample covarianceMatrices.