Design of Iterative Fuzzy Radial Basis Function Neural Networks Based on Iterative Weighted Fuzzy C-Means Clustering and Weighted LSE Estimation

In this article, a reinforced iterative fuzzy radial basis function neural networks (IFRBFNN) is introduced as an augmented FRBFNN architecture generated through the iterative refinement process of weighted fuzzy C-means (WFCM) clustering and weighted least square error estimation. It is well known...

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Vydáno v:	IEEE transactions on fuzzy systems Ročník 30; číslo 10; s. 4273 - 4285
Hlavní autoři:	Roh, Seok-Beom, Oh, Sung-Kwun, Pedrycz, Witold, Wang, Zheng, Fu, Zunwei, Seo, Kisung
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.10.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Classification Classification algorithms Clustering Clustering algorithms Coefficients Cross-entropy loss value Entropy Entropy (Information theory) Error analysis Estimation Fuzzy logic Fuzzy neural networks Iterative methods iterative radial basis function neural networks iterative weighted fuzzy c-means (WFCM) clustering Least squares Neural networks Polynomials Prototypes Radial basis function Radial basis function networks radial basis function neural networks (RBFNNs) Relocation Supervised learning Training data weighted least square error (WLSE) estimation
ISSN:	1063-6706, 1941-0034
On-line přístup:	Získat plný text
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