Full metadata record
DC FieldValueLanguage
dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorTalla, Jakub
dc.contributor.authorPeroutka, Zdeněk
dc.date.accessioned2021-03-15T11:00:30Z-
dc.date.available2021-03-15T11:00:30Z-
dc.date.issued2020
dc.identifier.citationJAMSHIDI, M. TALLA, J. PEROUTKA, Z.Deep learning techniques for model reference adaptive control and identification of complex systems. In: Proceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME 2020). Piscataway: IEEE, 2020. s. 147-153. ISBN 978-1-72815-602-6.cs
dc.identifier.isbn978-1-72815-602-6
dc.identifier.uri2-s2.0-85099299490
dc.identifier.urihttp://hdl.handle.net/11025/42948
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesProceedings of the 2020 19th International Conference on Mechatronics - Mechatronika (ME 2020)en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleDeep learning techniques for model reference adaptive control and identification of complex systemsen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedAlthough many mathematical and analytical techniques have been presented to control and identify the dynamic systems, there are vast fields of research needing to be developed and extended through Deep Learning (DL) approaches. In this paper, we try to describe how intelligent controllers can interact under control systems in a unique DL-based package. Despite the fact that conventional techniques have some advantages, such as the appropriate reliability and simple implementation for industrial goals, intelligent methods have potential to solve complex problems and identify nonlinear systems. Hence the concentration of this research is on the use of DL techniques to improve the system identification and control in model reference adaptive controllers. A dataset is also used to validate the responses of the proposed techniques. The simulation results demonstrate that not only are the proposed methods consistently appropriate to control the complex systems but also they have acceptable responses in order to utilize for system identification.en
dc.subject.translatedadaptive controlen
dc.subject.translatedartificial neural networksen
dc.subject.translateddeep learningen
dc.subject.translatedintelligent controlen
dc.subject.translatedsystem identificationen
dc.identifier.doi10.1109/ME49197.2020.9286698
dc.type.statusPeer-revieweden
dc.identifier.obd43931414
dc.project.IDEF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligencecs
Appears in Collections:Konferenční příspěvky / Conference Papers (KEV)
Konferenční příspěvky / Conference papers (RICE)
OBD

Files in This Item:
File SizeFormat 
Jamshidi_ME.pdf1,26 MBAdobe PDFView/Open    Request a copy


Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/42948

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

search
navigation
  1. DSpace at University of West Bohemia
  2. Publikační činnost / Publications
  3. OBD