Granular fuzzy models: analysis, design, and evaluation

DOI10.1016/j.ijar.2015.06.005zbMath1344.68240OpenAlexW810357897MaRDI QIDQ899183

Orion F. Reyes-Galaviz, Witold Pedrycz

Publication date: 21 December 2015

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2015.06.005

zbMATH Keywords

differential evolutionconditional fuzzy C-meansgranular fuzzy modelinggranularity optimizationinduced information granules

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (5)

Data-driven modeling with fuzzy sets and manifolds ⋮ Fuzzy C-means clustering based on dual expression between cluster prototypes and reconstructed data ⋮ Topological structures of \(L\)-fuzzy rough sets and similarity sets of \(L\)-fuzzy relations ⋮ Fuzzy information granular structures: a further investigation ⋮ GrNFS: a granular neuro-fuzzy system for regression in large volume data

Uses Software

• UCI-ml
• JStatCom
• KEEL
• ANFIS
• PANFIS
• DENFIS
• NEFCLASS-X

Cites Work

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• Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals' numbers (INs)
• Differential evolution. A practical approach to global optimization. With CD-ROM.
• Boosting of granular models
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• On the use of the weighted fuzzy c-means in fuzzy modeling
• Using interval information granules to improve forecasting in fuzzy time series
• Set-based granular computing: a lattice model
• Linguistic modelling based on semantic similarity relation among linguistic labels

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