From Fuzzy Rule-Based Models to Granular Models

Basit Öğe Kaydı
dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorCui, Ye
dc.contributor.authorHanyu, E.
dc.contributor.authorPedrycz, Witold
dc.contributor.authorLi, Zhiwu
dc.date.accessioned2025-04-18T10:32:47Z
dc.date.available2025-04-18T10:32:47Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractFuzzy rule-based models constructed in the presence of numeric data are nonlinear numeric models producing for any input some numeric output. There are no ideal models so the obtained numeric output could create a false illusion of achieved accuracy. A desirable approach is to augment the results with some measure of confidence (credibility) by admitting a granular rather than numeric format of the produced output values of the model. Our focus of this study is on fuzzy Takagi–Sugeno rule-based models whose conclusions are constant. The ultimate objective is to extend such models to the generalized granular structure with the conclusions formed as information granules. We study information granules described by intervals and fuzzy sets as well as probabilistic Gaussian information granules. The original design of the granular model is realized by involving the principle of justifiable granularity. Using this principle, we also show how to determine the equivalence between information granules. The construction of probabilistic information granules of the model is completed with the aid of optimized Gaussian process models. The granular models built in this way constitute a substantial and application-oriented departure from the numeric fuzzy models by offering a comprehensive insight into the quality of the produced results. The experimental studies based on synthetic and publicly available data demonstrate the design process and discuss the quality of the obtained results. © 2024 IEEE.
dc.identifier.citationCui, Y., Hanyu, E., Pedrycz, W., Fayek, A. R., Li, Z., & Wang, X. (2024). From Fuzzy Rule-Based Models to Granular Models. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3483817
dc.identifier.endpage656
dc.identifier.issn10636706
dc.identifier.issue2
dc.identifier.scopusqualityQ1
dc.identifier.startpage644
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3483817
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7116
dc.identifier.volume32
dc.identifier.wosWOS:001416196300015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGaussian Process
dc.subjectGranular Model
dc.subjectİnformation Granüle
dc.subjectRule-based Model
dc.subjectThe Principle of Justifiable Granularity
dc.titleFrom Fuzzy Rule-Based Models to Granular Models
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
From_Fuzzy_Rule-Based_Models_to_Granular_Models.pdf
Boyut:
3.66 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu