Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis

DOI10.1080/03081070701500059zbMath1160.68486OpenAlexW1972471335MaRDI QIDQ3603681

Edwin Lughofer, Plamen P. Angelov

Publication date: 18 February 2009

Published in: International Journal of General Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03081070701500059

zbMATH Keywords

incremental learningrule learningadaptation of parametersonline identificationevolving Takagi-Sugeno fuzzy systems

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42)

Related Items (1)

An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems

Uses Software

• ElemStatLearn

Cites Work

• Unnamed Item
• Evolving rule-based models. A tool for design of flexible adaptive systems.
• Flexible models with evolving structure
• The elements of statistical learning. Data mining, inference, and prediction

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