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Combined Parametric-Nonparametric Identification of Block-Oriented Systems [electronic resource] / by Grzegorz Mzyk.

By: Mzyk, Grzegorz [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Control and Information Sciences: 454Publisher: Cham : Springer International Publishing : Imprint: Springer, 2014Description: XVI, 238 p. 68 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319035963.Subject(s): Engineering | System theory | Statistical physics | Control engineering | Engineering | Control | Systems Theory, Control | Nonlinear DynamicsAdditional physical formats: Printed edition:: No titleDDC classification: 629.8 Online resources: Click here to access online
Contents:
Hammerstein system -- Wiener system -- Wiener-Hammerstein (sandwich) system -- Large-scale interconnected systems -- Structure detection and model order selection -- Time-varying systems -- Simulation studies -- Summary.
In: Springer eBooksSummary: This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.
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Hammerstein system -- Wiener system -- Wiener-Hammerstein (sandwich) system -- Large-scale interconnected systems -- Structure detection and model order selection -- Time-varying systems -- Simulation studies -- Summary.

This book considers a problem of block-oriented nonlinear dynamic system identification in the presence of random disturbances. This class of systems includes various interconnections of linear dynamic blocks and static nonlinear elements, e.g., Hammerstein system, Wiener system, Wiener-Hammerstein ("sandwich") system and additive NARMAX systems with feedback. Interconnecting signals are not accessible for measurement. The combined parametric-nonparametric algorithms, proposed in the book, can be selected dependently on the prior knowledge of the system and signals. Most of them are based on the decomposition of the complex system identification task into simpler local sub-problems by using non-parametric (kernel or orthogonal) regression estimation. In the parametric stage, the generalized least squares or the instrumental variables technique is commonly applied to cope with correlated excitations. Limit properties of the algorithms have been shown analytically and illustrated in simple experiments.

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