| Author | Title | Year | Journal/Proceedings | DOI/URL | |
|---|---|---|---|---|---|
| Dürrbaum, A., Kahl, M., Himmelsbach, M. & Kroll, A. | Toolbox zur Identifikation von Takagi-Sugeno-Fuzzy-Modellen [BibTeX] |
2021 | at -- Automatisierungstechnik , Vol. 69 (10) , pp. 915-916 | DOI | |
BibTeX:
@article{2021-Duerrbaum-at-Forum_TS_Toolbox,
author = {Axel Dürrbaum and Matthias Kahl and Matthias Himmelsbach and Andreas Kroll}
, title = {Toolbox zur Identifikation von Takagi-Sugeno-Fuzzy-Modellen}
, journal = {at -- Automatisierungstechnik}
, year = {2021}
, volume = {69}
, number = {10}
, pages = {915--916}
, doi = {https://doi.org/10.1515/auto-2021-0079}
}
|
|||||
| Gringard, M. & Kroll, A. | On considering the output in space-filling test signal designs for the identification of dynamic Takagi-Sugeno models | 2020 | 21st IFAC World Congress , Vol. 53 (2) , pp. 1200-1205 , Elsevier , Berlin, Germany , 12-17. July , IFAC | URL | |
| Abstract: In this contribution the optimal experiment design for the identification of linear affine Takagi-Sugeno (TS) models is discussed. The parameters of these models can be separated into local model and partition parameters which also allows for a separation of the design. The presented optimal design is based on the Fisher Information Matrix (FIM) and its objective is to minimize the parameter estimation uncertainty. In this contribution parameters of a standard test signal type (multi-step) are optimized to achieve this objective. The effectivity of FIM-based designs for nonlinear models depends on initial identification including structural decisions. The general functionality of the presented method is demonstrated on a case study. | |||||
BibTeX:
@inproceedings{GringardIFAC2020,
author = {Matthias Gringard and Andreas Kroll}
, title = {On considering the output in space-filling test signal designs for the identification of dynamic Takagi-Sugeno models}
, booktitle = {21st IFAC World Congress}
, publisher = {Elsevier}
, year = {2020}
, volume = {53}
, number = {2}
, pages = {1200-1205}
, address = {Berlin, Germany}
, url = {https://www.ifac2020.org/}
}
|
|||||
| Gringard, M. & Kroll, A. | Zur Homogenisierung von Testsignalen für die nichtlineare Systemidentifikation [BibTeX] |
2019 | at -- Automatisierungstechnik , Vol. 67 (10) , pp. 820-832 | ||
BibTeX:
@article{GringardAT2019,
author = {Matthias Gringard and Andreas Kroll}
, title = {Zur Homogenisierung von Testsignalen für die nichtlineare Systemidentifikation}
, journal = {at -- Automatisierungstechnik}
, year = {2019}
, volume = {67}
, number = {10}
, pages = {820--832}
}
|
|||||
| Gringard, M. & Kroll, A. | Zum optimalen online Testsignalentwurf für die Identifikation dynamischer TS-Modelle: Steuerfunktionen zur optimalen Schätzung der Partitionsparameter [BibTeX] |
2018 | 28. Workshop Computational Intelligence , pp. 39 - 60 , KIT Scientific Publishing , Dortmund , 29.-30. November , GMA-FA 5.14 | DOI URL | |
BibTeX:
@inproceedings{GringardGMA2018,
author = {Matthias Gringard and Andreas Kroll}
, title = {Zum optimalen online Testsignalentwurf für die Identifikation dynamischer TS-Modelle: Steuerfunktionen zur optimalen Schätzung der Partitionsparameter}
, booktitle = {28. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2018}
, pages = {39 -- 60}
, address = {Dortmund}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html}
, doi = {http://dx.doi.org/10.5445/KSP/1000085935}
, isbn = {9783731508458}
}
|
|||||
| Gringard, M. & Kroll, A. | Optimal Experiment Design for Identifying Dynamical Takagi-Sugeno Models with Minimal Parameter Uncertainty | 2018 | 18th IFAC Symposium on System Identification , Stockholm, Sweden , 09-11. July , IFAC | URL | |
| Abstract: In this contribution the optimal experiment design for the identification of linear affine Takagi-Sugeno (TS) models is discussed. The parameters of these models can be separated into local model and partition parameters which also allows for a separation of the design. The presented optimal design is based on the Fisher Information Matrix (FIM) and its objective is to minimize the parameter estimation uncertainty. In this contribution parameters of a standard test signal type (multi-step) are optimized to achieve this objective. The effectivity of FIM-based designs for nonlinear models depends on initial identification including structural decisions. The general functionality of the presented method is demonstrated on a case study. | |||||
BibTeX:
@inproceedings{GringardSYSID2018,
author = {Matthias Gringard and Andreas Kroll}
, title = {Optimal Experiment Design for Identifying Dynamical Takagi-Sugeno Models with Minimal Parameter Uncertainty}
, booktitle = {18th IFAC Symposium on System Identification}
, year = {2018}
, address = {Stockholm, Sweden}
, url = {https://www.ifac-control.org/events/system-identification-18th-sysid-2018}
}
|
|||||
| Gringard, M. & Kroll, A. | Zum Optimalen Offline Testsignalentwurf für die Identifikation dynamischer TS-Modelle: Multistufensignale für unsicherheitsminimierte Konklusionsparameter [BibTeX] |
2017 | 27. Workshop Computational Intelligence , KIT Scientific Publishing , Dortmund , 23.-24. November , GMA-FA 5.14 | DOI URL | |
BibTeX:
@inproceedings{GringardGMA2017,
author = {Matthias Gringard and Andreas Kroll}
, title = {Zum Optimalen Offline Testsignalentwurf für die Identifikation dynamischer TS-Modelle: Multistufensignale für unsicherheitsminimierte Konklusionsparameter}
, booktitle = {27. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2017}
, address = {Dortmund}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html}
, doi = {http://dx.doi.org/10.5445/KSP/1000085935}
, isbn = {9783731507260}
}
|
|||||
| Gringard, M. & Kroll, A. | On the systematic parametrization of APRBS and multisine test signals for nonlinear system identification [BibTeX] |
2016 | 26. Workshop Computational Intelligence , pp. 119-138 , KIT Scientific Publishing , Dortmund , 24.-25. November , GMA-FA 5.14 | DOI URL | |
BibTeX:
@inproceedings{GringardGMA2016,
author = {Matthias Gringard and Andreas Kroll}
, title = {On the systematic parametrization of APRBS and multisine test signals for nonlinear system identification}
, booktitle = {26. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2016}
, pages = {119-138}
, address = {Dortmund}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss}
, doi = {http://dx.doi.org/10.5445/KSP/1000060007}
, isbn = {9783731505884}
}
|
|||||
| Gringard, M. & Kroll, A. | On the parametrization of APRBS and multisine test signals for the identification of nonlinear dynamic TS-models [BibTeX] |
2016 | IEEE Symposium Series of Computational Intelligence 2016 , pp. 39 - 60 , Athens, Greece , 06.-09. December , IEEE | URL | |
BibTeX:
@inproceedings{GringardSSCI2016,
author = {Matthias Gringard and Andreas Kroll}
, title = {On the parametrization of APRBS and multisine test signals for the identification of nonlinear dynamic TS-models}
, booktitle = {IEEE Symposium Series of Computational Intelligence 2016}
, year = {2016}
, pages = {39 -- 60}
, address = {Athens, Greece}
, url = {http://ssci2016.cs.surrey.ac.uk/IEEE%202016.htm}
}
|
|||||
| Gringard, M. & Kroll, A. | Zur Homogenisierung von Breitbandtestsignalen für die nichtlineare Systemidentifikation am Beispiel eines nichtlinearen Stellantriebs [BibTeX] |
2015 | 25. Workshop Computational Intelligence , pp. 145 - 162 , KIT Scientific Publishing , Dortmund , 26.-27. November , GMA-FA 5.14 | DOI URL | |
BibTeX:
@inproceedings{GringardGMA2015,
author = {Matthias Gringard and Andreas Kroll}
, title = {Zur Homogenisierung von Breitbandtestsignalen für die nichtlineare Systemidentifikation am Beispiel eines nichtlinearen Stellantriebs}
, booktitle = {25. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2015}
, pages = {145 -- 162}
, address = {Dortmund}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html}
, doi = {http://dx.doi.org/0.5445/KSP/100004962}
, isbn = {9783731504320}
}
|
|||||
| Himmelsbach, M. & Kroll, A. | Testsignalentwurf mit Nutzung strukturellen Vorwissens zur Identifikation dynamischer lokal-affiner Takagi-Sugeno-Modelle | 2021 | at -- Automatisierungstechnik , accepted | ||
| Abstract: In diesem Beitrag wird eine Methode zum Entwurf von Testsignalen für die Identifikation lokal-affiner dynamischer Takagi-Sugeno-SISO-Modelle vorgestellt. Diese Methode verwendet ein zuvor identifizierten Prozessmodell und nutzt so erlangtes Wissen über die Struktur mittels modellprädiktiver Regelung aus. Im Rahmen einer akademischen Fallstudie wird die Methode demonstriert. | |||||
BibTeX:
@article{2021-MH-at-MPC,
author = {Matthias Himmelsbach and Andreas Kroll}
, title = {Testsignalentwurf mit Nutzung strukturellen Vorwissens zur Identifikation dynamischer lokal-affiner Takagi-Sugeno-Modelle}
, journal = {at -- Automatisierungstechnik}
, year = {2021}
, note = {accepted}
}
|
|||||
| Himmelsbach, M. & Kroll, A. | On optimal test signal design and parameter identification schemes for dynamic Takagi-Sugeno fuzzy models using the Fisher information matrix [BibTeX] |
2021 | International Journal of Fuzzy Systems , accepted | ||
BibTeX:
@article{HimmelsbachIJFS2021,
author = {Matthias Himmelsbach and Andreas Kroll}
, title = {On optimal test signal design and parameter identification schemes for dynamic Takagi-Sugeno fuzzy models using the Fisher information matrix}
, journal = {International Journal of Fuzzy Systems}
, year = {2021}
, note = {accepted}
}
|
|||||
| Himmelsbach, M. & Kroll, A. | Toolbox zum Testsignalentwurf für Standardtestsignale für die Identifikation von Eingrößensystemen: Prozessmodellfreie und -basierte Methoden [BibTeX] |
2020 | 30. Workshop Computational Intelligence , KIT Scientific Publishing , Berlin , 26.-27. November , GMA-FA 5.14 | URL | |
BibTeX:
@inproceedings{HimmelsbachGMACI2020,
author = {Matthias Himmelsbach and Andreas Kroll}
, title = {Toolbox zum Testsignalentwurf für Standardtestsignale für die Identifikation von Eingrößensystemen: Prozessmodellfreie und -basierte Methoden}
, booktitle = {30. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2020}
, address = {Berlin}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html}
}
|
|||||
| Kroll, A. & Himmelsbach, M. | Zum optimalen Testsignalentwurf für die Identifkation regelungsorientierter dynamischer empirischer lokal linear-affiner Multi-Modelle [BibTeX] |
2021 | (KR3795/7-1) , Schlussbericht | ||
BibTeX:
@techreport{2021-Abschlusschlussbericht-DFG-Testsignalentwurf,
author = {Andreas Kroll and Matthias Himmelsbach}
, title = {Zum optimalen Testsignalentwurf für die Identifkation regelungsorientierter dynamischer empirischer lokal linear-affiner Multi-Modelle}
, type = {Schlussbericht}
, year = {2021}
, number = {KR3795/7-1}
}
|
|||||
| Wittich, F., Gringard, M., Kahl, M., Kroll, A., Niendorf, T. & Zinn, W. | Datengetriebene Modellierung zur Prädiktion des Eigenspannungstiefenverlaufs beim Hartdrehen [BibTeX] |
2018 | 28. Workshop Computational Intelligence , pp. 61 - 81 , KIT Scientific Publishing , Dortmund , 29.-30. November , GMA-FA 5.14 | URL | |
BibTeX:
@inproceedings{WittichGMA2018,
author = {Felix Wittich and Matthias Gringard and Matthias Kahl and Andreas Kroll and Thomas Niendorf and Wolfgang Zinn}
, title = {Datengetriebene Modellierung zur Prädiktion des Eigenspannungstiefenverlaufs beim Hartdrehen}
, booktitle = {28. Workshop Computational Intelligence}
, publisher = {KIT Scientific Publishing}
, year = {2018}
, pages = {61 -- 81}
, address = {Dortmund}
, url = {http://www.rst.e-technik.tu-dortmund.de/cms/de/Veranstaltungen/GMA-Fachausschuss/index.html}
}
|
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