Open Access
Identification of Linear Systems with Nonlinear Distortions
Reads0
Chats0
TLDR
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.read more
Citations
More filters
Book ChapterDOI
Frequency Domain Tracking of Time-Varying Modes
John Lataire,Rik Pintelon +1 more
TL;DR: In this article, the authors provide a methodology for tracking the evolving dynamics of linear, slowly time-varying systems, and explain the responses of the systems to multisine excitations.
Book ChapterDOI
Nonparametric Analysis and Nonlinear State-Space Identification: A Benchmark Example
TL;DR: In this paper, the final goal is to construct a nonlinear state-space model, but first, it is shown how to retrieve a lot of information via one (or few) multisine experiments.
Detection of nonlinearities when measuring respiratory impedance
TL;DR: The forced oscillation technique (FOT) as mentioned in this paper employs small-amplitude pressure oscillations superimposed on the normal breathing to measure respiratory mechanics, which results in minimizing the influence of the nonlinearities (NL) coming from the device.
Journal ArticleDOI
Electrochemical impedance spectroscopy beyond linearity and stationarity - a critical review
Noel Hallemans,David A. Howey,Alberto Battistel,Nessa Fereshteh Saniee,Federico Scarpioni,Benny Wouters,Fabio La Mantia,Annick Hubin,Widanalage Dhammika Widanage,John Lataire +9 more
TL;DR: In this article , the concept of impedance beyond linearity and stationarity was reviewed and different methods to estimate this from measured current and voltage data, with emphasis on frequency domain approaches using multisine excitation.
References
More filters
Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
Book
Engineering Applications of Correlation and Spectral Analysis
TL;DR: This chapter discusses single-Input/Single-Output Relationships, nonstationary data analysis techniques, and procedures to Solve Multiple- Input/Multiple-Output Problems.
Journal ArticleDOI
Nonlinear black-box modeling in system identification: a unified overview
Jonas Sjöberg,Qinghua Zhang,Lennart Ljung,Albert Benveniste,Bernard Delyon,Pierre-Yves Glorennec,Håkan Hjalmarsson,Anatoli Juditsky +7 more
TL;DR: What are the common features in the different approaches, the choices that have to be made and what considerations are relevant for a successful system-identification application of these techniques are described, from a user's perspective.