On the use of the weighted fuzzy c-means in fuzzy modeling

DOI10.1016/j.advengsoft.2004.12.001zbMath1075.68641OpenAlexW2046815126WikidataQ62356901 ScholiaQ62356901MaRDI QIDQ1776380

George E. Tsekouras

Publication date: 12 May 2005

Published in: Advances in Engineering Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.advengsoft.2004.12.001

zbMATH Keywords

Fuzzy modelingCovariance matrixBack-propagationLearning vector quantizationOrthogonal least squaresWeighted fuzzy c-means

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (9)

Identification of fuzzy models using a successive tuning method with a variant identification ratio ⋮ A clustering algorithm based TS fuzzy model for tracking dynamical system data ⋮ Granular fuzzy models: analysis, design, and evaluation ⋮ Identification of fuzzy relation models using hierarchical fair competition-based parallel genetic algorithms and information granulation ⋮ A novel T-S fuzzy systems identification with block structured sparse representation ⋮ Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation ⋮ Dynamic T-S Fuzzy Systems Identification Based on Sparse Regularization ⋮ A novel identification method for Takagi-Sugeno fuzzy model ⋮ Fuzzy radial basis function neural networks with information granulation and its parallel genetic optimization

This page was built for publication: On the use of the weighted fuzzy c-means in fuzzy modeling