Rule-based fuzzy neural networks realized with the aid of linear function Prototype-driven fuzzy clustering and layer Reconstruction-based network design strategy

In this study, we introduce novel fuzzy neural networks designed with the aid of linear function prototype-driven fuzzy clustering (LFPFC) and layer reconstruction-based network design strategy to deal with the regression problem. The LFPFC constitutes a new clustering technique inspired by the fuzz...

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Vydané v:	Expert systems with applications Ročník 219; s. 119655
Hlavní autori:	Park, Sang-Beom, Oh, Sung-Kwun, Kim, Eun-Hu, Pedrycz, Witold
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.06.2023
Predmet:	Distance-based LFPFC Estimated output-based LFPFC Fuzzy c-regression model (FCRM) clustering Layer reconstruction-based network design strategy Linear function prototype-driven fuzzy clustering (LFPFC) Distance-based LFPFC Estimated output-based LFPFC Linear function prototype-driven fuzzy clustering (LFPFC) Layer reconstruction-based network design strategy Fuzzy c-regression model (FCRM) clustering
ISSN:	0957-4174, 1873-6793
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