Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6652
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Çelikyılmaz, Aslı | - |
dc.contributor.author | Türkşen, İsmail Burhan | - |
dc.date.accessioned | 2021-09-11T15:43:04Z | - |
dc.date.available | 2021-09-11T15:43:04Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.citation | Annual Meeting of the North-American-Fuzzy-Information-Processing-Society -- JUN 24-27, 2007 -- San Diego, CA | en_US |
dc.identifier.isbn | 978-1-4244-1213-6 | - |
dc.identifier.uri | https://doi.org/10.1109/NAFIPS.2007.383826 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6652 | - |
dc.description.abstract | A new Fuzzy System Modeling (FSM) approach based on Improved Fuzzy Functions using Discrete Interval Type 2 Fuzzy Sets is presented. The new method is proposed as an alternate learning and reasoning schema to Type 1 and Type 2 FSM with Fuzzy Rule Base (FRB) approaches and enhances Type 2 FSM by reducing complexity and increasing prediction performance. Structure identification of the new approach is based on a supervised Improved Fuzzy Clustering (IFC) method with a dual optimization algorithm, which yields improved membership values. The merit of the proposed Type 2 FSM is that uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy functions. The uncertainty in selection of the learning parameters are captured by identifying two separate features: executing IFC method with varying levels of fuzziness values, m, and collection of different fuzzy function structures. It is shown with an empirical study that the new Type 2 FSM approach is superior in comparison to earlier Type 1 and Type 2 FSMs in terms of robustness and error reduction. | en_US |
dc.description.sponsorship | N Amer Fuzzy Informat Proc Soc, IEEE | en_US |
dc.description.sponsorship | NSERCNatural Sciences and Engineering Research Council of Canada (NSERC); OGSST | en_US |
dc.description.sponsorship | This work is partially supported by NSERC and OGSST Grants. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | Nafips 2007 - 2007 Annual Meeting of The North American Fuzzy Information Processing Society | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | [No Keywords] | en_US |
dc.title | Enhanced Type 2 Fuzzy System Models With Improved Fuzzy Functions | en_US |
dc.type | Conference Object | 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.startpage | 140 | en_US |
dc.identifier.endpage | + | en_US |
dc.identifier.wos | WOS:000248517100026 | en_US |
dc.identifier.scopus | 2-s2.0-35148836676 | en_US |
dc.institutionauthor | Türkşen, İsmail Burhan | - |
dc.identifier.doi | 10.1109/NAFIPS.2007.383826 | - |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.relation.conference | Annual Meeting of the North-American-Fuzzy-Information-Processing-Society | en_US |
item.openairetype | Conference Object | - |
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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