A two-experiment approach to Wiener system identification

Autor: Giulio Bottegal, Johan A. K. Suykens, Ricardo Castro-Garcia

Publikováno v: Automatica, 93, 282-289. Elsevier

We propose a new methodology for identifying Wiener systems using the data acquired from two separate experiments. In the first experiment, we feed the system with a sinusoid at a prescribed frequency and use the steady state response of the system t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::483f0c1841a75a77584d5d4836ca282c
https://doi.org/10.1016/j.automatica.2018.03.069

Impulse Response Constrained LS-SVM modeling for Hammerstein System Identification * *EU: The research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) / ERC AdG A-DATADRIVE-B (290923). This paper reflects only the authors views and the Union is not liable for any use that may be made of the contained information. Research Council KUL: CoE PFV/10/002 (OPTEC), BIL12/11T; PhD/Postdoc grants Flemish Government: FWO: projects: G.0377.12 (Structured systems), G.088114N (Tensor based data similarity); PhD/Postdoc grant iMinds Medical Information Technologies SBO 2015 IWT: POM II SBO 100031 Belgian Federal Science Policy Office: IUAP P7/19 (DYSCO, Dynamical systems, control and optimization, 2012-2017)

Autor: Ricardo Castro-Garcia, Johan A. K. Suykens, Oscar Mauricio Agudelo

Publikováno v: IFAC-PapersOnLine. 50:14046-14051

Hammerstein systems are composed by a static nonlinearity followed by a linear dynamic system. The proposed method for identifying Hammerstein systems consists of a formulation within the Least Squares Support Vector Machines (LS-SVM) framework where

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8e40967c71598f444cd1ca690b368c41
https://doi.org/10.1016/j.ifacol.2017.08.2435

On the identification of Wiener systems with polynomial nonlinearity

Autor: Ricardo Castro-Garcia, Giulio Bottegai, Johan A. K. Suykens

Publikováno v: 2017 IEEE 56th Annual Conference on Decision and Control (CDC) : December 12-15, 2017, Melbourne, Australia, 6475-6480
STARTPAGE=6475;ENDPAGE=6480;TITLE=2017 IEEE 56th Annual Conference on Decision and Control (CDC) : December 12-15, 2017, Melbourne, Australia
CDC

© 2017 IEEE. In this paper we introduce a new method for Wiener system identification that relies on the data collected on two separate experiments. In the first experiment, the system is excited with a sine signal at fixed frequency and phase shift

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6080740b6c0150ddb227ec7641e2d5f6
https://research.tue.nl/nl/publications/d60ad6dc-96f7-4684-a208-6463f22c42b9

Wiener System Identification using Best Linear Approximation within the LS-SVM framework

Autor: Ricardo Castro-Garcia, Johan A. K. Suykens

Publikováno v: 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI).

Wiener systems represent a linear time invariant (LTI) system followed by a static nonlinearity. The identification of these systems has been a research problem for a long time as it is not a trivial task. A new methodology for identifying Wiener sys

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cc5142cf893b1dfcdc97d016a8735e24
https://doi.org/10.1109/la-cci.2016.7885698

Incorporating best linear approximation within LS-SVM-based Hammerstein system identification

Autor: Johan Schoukens, Ricardo Castro-Garcia, Johan A. K. Suykens, Koen Tiels

Publikováno v: CDC
Vrije Universiteit Brussel

Hammerstein systems represent the coupling of a static nonlinearity and a linear time invariant (LTI) system. The identification problem of such systems has been a focus of research during a long time as it is not a trivial task. In this paper a meth

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78e46569a1ef4005a3c825f07598cc8c
https://biblio.vub.ac.be/vubir/incorporating-best-linear-approximation-within-lssvmbased-hammerstein-system-identification(cd3cf43f-631e-4a0d-9aed-f8fc94ac3df0).html