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https://hdl.handle.net/20.500.11851/7405
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.date.accessioned | 2021-09-11T15:56:50Z | - |
dc.date.available | 2021-09-11T15:56:50Z | - |
dc.date.issued | 2009 | en_US |
dc.identifier.issn | 0142-3312 | - |
dc.identifier.issn | 1477-0369 | - |
dc.identifier.uri | https://doi.org/10.1177/0142331208090627 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/7405 | - |
dc.description.abstract | Fuzzy system modelling (FSM) is one of the most prominent tools that can be used to identify the behaviour of highly non-linear systems with uncertainty. In the past, FSM techniques utilized Type 1 fuzzy sets in order to capture the uncertainty in the system. However, since Type I fuzzy sets express the belongingness of a crisp value x' of an input variable x in a fuzzy set A by a crisp membership value mu(A)(x'), they cannot fully capture the uncertainties associated with higher-order imprecisions in identifying membership functions. In the future, we are likely to observe higher types of fuzzy sets, such as Type 2 fuzzy sets. The use of Type 2 fuzzy sets and linguistic logical connectives has drawn a considerable amount of attention in the realm of FSM in the last two decades. In this paper, we first review Type I fuzzy system models known as Zadeh, Takagi-Sugeno and Turksen models; then we review potentially future realizations of Type 2 fuzzy systems again under the headings of Zadeh, Takagi-Sugeno and Turksen fuzzy system models, in contrast to Type I fuzzy system models. Zadeh's and Takagi-Sugeno's models are essentially fuzzy rule base (FRB) models, whereas Turksen's models are essentially fuzzy function (FF) models. Type 2 fuzzy system models have a higher predictive power. One of the essential problems of Type 2 fuzzy system models is computational complexity. In data-driven FSM methods discussed here, a fuzzy C-means (FCM) clustering algorithm is used in order to identify the system structure, ie, either the number of fuzzy rules or alternately the number of FFs. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Sage Publications Ltd | en_US |
dc.relation.ispartof | Transactions of The Institute of Measurement And Control | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | fuzzy clustering | en_US |
dc.subject | fuzzy functions | en_US |
dc.subject | fuzzy system models | en_US |
dc.subject | Type 1 and 2 fuzzy system models | en_US |
dc.title | Review of Fuzzy System Models With an Emphasis on Fuzzy Functions | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Industrial Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 31 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 7 | en_US |
dc.identifier.endpage | 31 | en_US |
dc.identifier.wos | WOS:000263421300002 | en_US |
dc.identifier.scopus | 2-s2.0-58349116891 | en_US |
dc.institutionauthor | Türkşen, İsmail Burhan | - |
dc.identifier.doi | 10.1177/0142331208090627 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q3 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | Endüstri Mühendisliği Bölümü / Department of Industrial Engineering Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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