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https://hdl.handle.net/11147/12476
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kucukoglu, Sefa Furkan | - |
dc.contributor.author | Dede, Mehmet Ismet Can | - |
dc.contributor.author | Ceccarelli, Marco | - |
dc.date.accessioned | 2022-09-26T07:37:48Z | - |
dc.date.available | 2022-09-26T07:37:48Z | - |
dc.date.issued | 2022 | - |
dc.identifier.isbn | 9783031107757 | - |
dc.identifier.isbn | 9783031107764 | - |
dc.identifier.issn | 2211-0984 | - |
dc.identifier.issn | 2211-0992 | - |
dc.identifier.uri | https://doi.org/10.1007/978-3-031-10776-4_25 | - |
dc.description.abstract | Identifying the model of a magneto-rheological (MR) fluid-based brake is extremely important for designing and controlling a haptic device with hybrid actuation. Therefore, in this study, an Elman Recurrent Neural Network (ERNN) is designed to understand and model a characterization of an MR fluid-based rotational brake. Three important factors that affect the MR brake's performance are chosen as inputs: current, speed, and the first derivative of the input current. The proposed network is trained, and the performance of the network is tested with three different experimental scenarios. Then, the effect of these inputs on the system is investigated. According to the results, it can be said that the designed ERNN is a good candidate for modelling an MR brake. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Springer international Publishing Ag | en_US |
dc.relation.ispartof | 4th International Conference of International-Federation-for-the-Promotion-of-Mechanism-and-Machine-Science ITALY (IFToMM ITALY) -- SEP 07-09, 2022 -- Univ Napoli, Naples, ITALY | en_US |
dc.relation.ispartofseries | Mechanisms and Machine Science | - |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Magneto-Rheological Fluid-Based Brake | en_US |
dc.subject | Elman Recurrent Neural Network | en_US |
dc.subject | Haptic Device | en_US |
dc.subject | Hybrid Actuation System | en_US |
dc.title | Modeling a Magneto-Rheological Fluid-Based Brake Via a Neural Network Method | en_US |
dc.type | Conference Object | en_US |
dc.authorid | 0000-0001-8083-2306 | - |
dc.authorid | 0000-0001-6220-6678 | - |
dc.institutionauthor | Küçükoğlu, Sefa Furkan | - |
dc.institutionauthor | Dede, Mehmet İsmet Can | - |
dc.department | İzmir Institute of Technology | en_US |
dc.identifier.volume | 122 | en_US |
dc.identifier.startpage | 211 | en_US |
dc.identifier.endpage | 218 | en_US |
dc.identifier.wos | WOS:001346884500025 | - |
dc.identifier.scopus | 2-s2.0-85135843773 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | 4th International Conference of the IFToMM Italy, IFIT 2022 | en_US |
dc.relation.publication | Advances in Italian Mechanism Science: Proceedings of the 4th International Conference of IFToMM Italy | en_US |
dc.identifier.doi | 10.1007/978-3-031-10776-4_25 | - |
dc.relation.isbn | 978-303110775-7 | en_US |
dc.relation.doi | 10.1007/978-3-031-10776-4 | en_US |
dc.relation.issn | 2211-0984 | en_US |
dc.description.volume | 122 MMS | en_US |
dc.description.startpage | 211 | en_US |
dc.description.endpage | 218 | en_US |
dc.authorwosid | Dede, Mehmet/Aft-9321-2022 | - |
dc.authorwosid | Kucukoglu, Sefa/Aek-1924-2022 | - |
dc.identifier.wosquality | N/A | - |
dc.identifier.scopusquality | Q4 | - |
dc.description.woscitationindex | Conference Proceedings Citation Index - Science | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairetype | Conference Object | - |
crisitem.author.dept | 03.10. Department of Mechanical Engineering | - |
Appears in Collections: | Mechanical Engineering / Makina Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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