Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6544
Full metadata record
DC FieldValueLanguage
dc.contributor.authorUncu, Özge-
dc.contributor.authorTürkşen, İsmail Burhan-
dc.date.accessioned2021-09-11T15:37:17Z-
dc.date.available2021-09-11T15:37:17Z-
dc.date.issued2007en_US
dc.identifier.issn1063-6706-
dc.identifier.issn1941-0034-
dc.identifier.urihttps://doi.org/10.1109/TFUZZ.2006.889765-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6544-
dc.description.abstract-Fuzzy system modeling (FSM) is one of the most prominent tools that can be used to identify the behavior of highly nonlinear systems with uncertainty. Conventional FSM techniques utilize type 1 fuzzy sets in order to capture the uncertainty in the system. However, since type 1 fuzzy sets express the belongingness of a crisp value x' of a base variable x in a fuzzy set A by a crisp membership value mu(A)(x'), they cannot fully capture the uncertainties due to imprecision in identifying membership functions. Higher types of fuzzy sets can be a remedy to address this issue. Since, the computational complexity of operations on fuzzy sets are increasing with the increasing type of the fuzzy set, the use-of type 2 fuzzy sets and linguistic logical connectives drew a considerable amount of attention in the realm of fuzzy system modeling in the last two decades. In this paper, we propose a black-box methodology that can identify robust type 2. Takagi-Sugeno, Mizumoto and Linguistic fuzzy system models with high predictive power. One of the essential problems of type 2 fuzzy system models is computational complexity. In order to remedy this problem, discrete interval valued type 2 fuzzy system models are proposed with type reduction. In the proposed fuzzy system modeling methods, fuzzy C-means (FCM) clustering algorithm is used in order to identify the system structure. The proposed discrete interval valued type 2 fuzzy system models are generated by a learning parameter of FCM, known as the level of membership, and its variation over a specific set of values which generate the uncertainty associated with the system structure.en_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Transactions On Fuzzy Systemsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfuzzy system modelsen_US
dc.subjectfuzzy inference systemsen_US
dc.subjectfuzzy clusteringen_US
dc.subjecttype 2 fuzzy system modelsen_US
dc.subjectlevel of fuzzinessen_US
dc.titleDiscrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parametersen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Industrial Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümütr_TR
dc.identifier.volume15en_US
dc.identifier.issue1en_US
dc.identifier.startpage90en_US
dc.identifier.endpage106en_US
dc.identifier.wosWOS:000244803400008en_US
dc.identifier.scopus2-s2.0-33947365579en_US
dc.institutionauthorTürkşen, İsmail Burhan-
dc.identifier.doi10.1109/TFUZZ.2006.889765-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
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
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

92
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

86
checked on Sep 21, 2024

Page view(s)

44
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


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