From Fuzzy Rule-Based Models to Granular Models
Yükleniyor...
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Fuzzy 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.
Açıklama
Anahtar Kelimeler
Gaussian Process, Granular Model, İnformation Granüle, Rule-based Model, The Principle of Justifiable Granularity
Kaynak
IEEE Transactions on Fuzzy Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
32
Sayı
2
Künye
Cui, 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.
