An on-line algorithm for creating self-organizing fuzzy neural networks

DOI10.1016/j.neunet.2004.07.009zbMath1073.68714OpenAlexW2022062543WikidataQ51582102 ScholiaQ51582102MaRDI QIDQ705601

Gang Leng, Girijesh Prasad, Thomas Martin McGinnity

Publication date: 31 January 2005

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2004.07.009

zbMATH Keywords

TS modelEBFRecursive least squares algorithmSelf-organizing fuzzy neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Direct adaptive fuzzy control with a self-structuring algorithm ⋮ Evolution computation based learning algorithms of polygonal fuzzy neural networks ⋮ Direct adaptive fuzzy control with membership function tuning ⋮ An evolving concept in the identification of an interval fuzzy model of Wiener-Hammerstein nonlinear dynamic systems ⋮ Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis ⋮ Simulation and evaluation of second-order fuzzy boundary value problems

Uses Software

• ANFIS

Cites Work

• Unnamed Item
• A constructive approach to neuro-fuzzy networks
• New similarity measures on fuzzy sets and on elements
• Neuro-fuzzy systems for function approximation
• Scale-based approach to hierarchical fuzzy clustering.
• A fast learning algorithm for parsimonious fuzzy neural systems
• Multilayer feedforward networks are universal approximators
• Problems of identification and control
• Fuzzy logic in control systems: fuzzy logic controller. II
• Fuzzy identification of systems and its applications to modeling and control
• Constructing fuzzy graphs from examples

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