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dc.contributor.authorBüchi, Roland-
dc.date.accessioned2023-09-14T08:51:04Z-
dc.date.available2023-09-14T08:51:04Z-
dc.date.issued2022-01-27-
dc.identifier.isbn978-3-8007-5656-8de_CH
dc.identifier.urihttps://ieeexplore.ieee.org/document/9698304de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28659-
dc.description.abstractIn the mathematical modeling of piezo elements and general actuators and sensors with hysteresis such as electromagnets, a method is often used that is based on the research results of Preisach and Mayergoyz. Local maxima and minima are saved and elementary hysteresis operators are weighted with an area function. This area function is the central element of this method because it determines the characteristics of the hysteresis of the piezo elements or electromagnets. It is often carried out with a defined measurement on the system. This article presents a new approach to determining the area function using a machine learning approach. Based on an initial weighting, the parameters are changed in small steps and compared with the measurement using the least square method. After a large number of iteration steps, the model fits better and better with the real system and the least square criterion is minimized. The parameters found at the end have the advantage that they can also be determined with measurements that match the dynamics of the signals occurring in the applications.de_CH
dc.language.isoende_CH
dc.publisherVDEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectPiezo hysteresisde_CH
dc.subjectMathematical modelde_CH
dc.subjectComputational intelligencede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc510: Mathematikde_CH
dc.titleMachine learning for mathematical modelling of Piezo hysteresisde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeBerlinde_CH
zhaw.conference.detailsMikroSystemTechnik Congress, Ludwigsburg, Germany, 8-10 November 2021de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end173de_CH
zhaw.pages.start170de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsMikroSystemTechnik Congress 2021 : Proceedingsde_CH
zhaw.webfeedSoftware Engineeringde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Büchi, R. (2022). Machine learning for mathematical modelling of Piezo hysteresis [Conference paper]. MikroSystemTechnik Congress 2021 : Proceedings, 170–173. https://ieeexplore.ieee.org/document/9698304
Büchi, R. (2022) ‘Machine learning for mathematical modelling of Piezo hysteresis’, in MikroSystemTechnik Congress 2021 : Proceedings. Berlin: VDE, pp. 170–173. Available at: https://ieeexplore.ieee.org/document/9698304.
R. Büchi, “Machine learning for mathematical modelling of Piezo hysteresis,” in MikroSystemTechnik Congress 2021 : Proceedings, Jan. 2022, pp. 170–173. [Online]. Available: https://ieeexplore.ieee.org/document/9698304
BÜCHI, Roland, 2022. Machine learning for mathematical modelling of Piezo hysteresis. In: MikroSystemTechnik Congress 2021 : Proceedings [online]. Conference paper. Berlin: VDE. 27 Januar 2022. S. 170–173. ISBN 978-3-8007-5656-8. Verfügbar unter: https://ieeexplore.ieee.org/document/9698304
Büchi, Roland. 2022. “Machine Learning for Mathematical Modelling of Piezo Hysteresis.” Conference paper. In MikroSystemTechnik Congress 2021 : Proceedings, 170–73. Berlin: VDE. https://ieeexplore.ieee.org/document/9698304.
Büchi, Roland. “Machine Learning for Mathematical Modelling of Piezo Hysteresis.” MikroSystemTechnik Congress 2021 : Proceedings, VDE, 2022, pp. 170–73, https://ieeexplore.ieee.org/document/9698304.


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