Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6180
Full metadata record
DC FieldValueLanguage
dc.contributor.authorZarandi, Mohammad Hossein Fazel-
dc.contributor.authorGamasaee, R.-
dc.contributor.authorTürkşen, İsmail Burhan-
dc.date.accessioned2021-09-11T15:35:11Z-
dc.date.available2021-09-11T15:35:11Z-
dc.date.issued2014en_US
dc.identifier.issn0268-3768-
dc.identifier.issn1433-3015-
dc.identifier.urihttps://doi.org/10.1007/s00170-013-5372-4-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6180-
dc.description.abstractIn this paper, a novel type-2 fuzzy expert system for prediction the amount of reagents in desulfurization process of a steel industry in Canada is developed. In this model, the new interval type-2 fuzzy c-regression clustering algorithm for structure identification phase of Takagi-Sugeno (T-S) systems is presented. Gaussian Mixture Model is used to generate partition matrix in clustering algorithm. Then, an interval type-2 hybrid fuzzy system, which is the combination of Mamdani and Sugeno method, is proposed. The new hybrid inference system uses fuzzy disjunctive normal forms and fuzzy conjunctive normal forms for aggregation of antecedents. A statistical test, which uses least square method, is implemented in order to select variables. In order to validate our method, we develop three system modeling techniques and compare the results with our proposed interval type-2 fuzzy hybrid expert system. These techniques are multiple regression, type-1 fuzzy expert system, and interval type-2 fuzzy TSK expert system. For tuning parameters of the system, adaptive-network-based fuzzy inference system is used. Finally, neural network is utilized in order to reduce error of the system. The results show that our proposed method has less error and high accuracy.en_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInterval type-2 fuzzy hybrid systemen_US
dc.subjectIT2F c-regression clusteringen_US
dc.subjectGaussian mixture modelen_US
dc.subjectFuzzyen_US
dc.titleA type-2 fuzzy expert system based on a hybrid inference method for steel industryen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Industrial Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümütr_TR
dc.identifier.volume71en_US
dc.identifier.issue5-8en_US
dc.identifier.startpage857en_US
dc.identifier.endpage885en_US
dc.identifier.wosWOS:000331985000008en_US
dc.identifier.scopus2-s2.0-84896732447en_US
dc.institutionauthorTürkşen, İsmail Burhan-
dc.identifier.doi10.1007/s00170-013-5372-4-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
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

5
checked on Mar 23, 2024

WEB OF SCIENCETM
Citations

4
checked on Mar 9, 2024

Page view(s)

14
checked on Mar 25, 2024

Google ScholarTM

Check




Altmetric


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