Journal ArticleDOI
Parametric and nonparametric identification of linear systems in the presence of nonlinear distortions-a frequency domain approach
TLDR
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.Abstract:
This paper studies the asymptotic behavior of nonparametric and parametric frequency domain identification methods to model linear dynamic systems in the presence of nonlinear distortions under some general conditions for random multisine excitations. In the first part, 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. In the second part a parametric model for the RLDS is identified. Convergence in probability of this model to the RLDS is proven. A function of dependency is defined to detect and separate the presence of unmodeled dynamics and nonlinear distortions and to bound the bias error on the transfer function estimate.read more
Citations
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Journal ArticleDOI
From experiment design to closed-loop control
TL;DR: It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy.
Journal ArticleDOI
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.
Journal ArticleDOI
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.
Journal ArticleDOI
Nonlinear System Analysis and Identification from Random Data
A. V. Metcalfe,Julius S. Bendat +1 more
TL;DR: This paper presents a meta-analysis of statistical errors in Nonlinear Estimates of Linear and Nonlinear Systems and their applications in Input/Output Relationships and Bilinear and Trilinear Systems.
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.
References
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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.
Book
Time Series: Data Analysis and Theory
TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.
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.
Book
The Volterra and Wiener Theories of Nonlinear Systems
TL;DR: In this article, a complete and detailed development of the analysis, design and characterization of non-linear systems using the Volterra and Wiener theories, as well as gate functions, is presented.