Identification of fuzzy models using a successive tuning method with a variant identification ratio

DOI10.1016/j.fss.2007.12.031zbMath1169.93320OpenAlexW1977184511MaRDI QIDQ835270

Jeoung-Nae Choi, Sung-Kwun Oh, Witold Pedrycz

Publication date: 28 August 2009

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2007.12.031

zbMATH Keywords

fuzzy modelC-means clusteringhierarchical fair competition (HFC)information granulationparallel genetic algorithms (PGA)successive tuning methodvariant identification ratio

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy control/observation systems (93C42) System identification (93B30)

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Uses Software

• ANFIS

Cites Work

• Unnamed Item
• Multilevel fuzzy relational systems: Structure and identification
• An identification algorithm in fuzzy relational systems
• The evaluation of fuzzy models derived from experimental data
• Classification, pattern recognition and reduction of dimensionality
• Multivariate adaptive regression splines
• Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems
• On the use of the weighted fuzzy c-means in fuzzy modeling
• Predicting a chaotic time series using a fuzzy neural network
• Granular neural networks
• Fuzzy identification of systems and its applications to modeling and control
• Fuzzy Model Identification and Self-Learning for Dynamic Systems
• SYNTHESIS OF FUZZY MODELS FOR INDUSTRIAL PROCESSES-SOME RECENT RESULTS
• Oscillation and Chaos in Physiological Control Systems