Interpretability and learning in neuro-fuzzy systems

DOI10.1016/j.fss.2003.11.012zbMath1068.68125OpenAlexW2165948284MaRDI QIDQ1885674

Rui Pedro Paiva, António Dourado

Publication date: 12 November 2004

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

Full work available at URL: https://doi.org/10.1016/j.fss.2003.11.012

zbMATH Keywords

ClusteringSystem identificationNeuro-fuzzy systemsTransparencyInterpretabilityFuzzy system models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy ⋮ On-line identification of computationally undemanding evolving fuzzy models ⋮ Fuzzy rule base simplification using multidimensional scaling and constrained optimization ⋮ The TaSe-NF model for function approximation problems: approaching local and global modelling ⋮ Logic-based fuzzy networks: a study in system modeling with triangular norms and uninorms

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• Three-objective genetics-based machine learning for linguistic rule extraction
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• Oscillation and Chaos in Physiological Control Systems
• ARE FUZZY SYSTEMS UNIVERSAL APPROXIMATORS?
• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
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