Improving the robustness of recursive consequent parameters learning in evolving neuro-fuzzy systems

DOI10.1016/j.ins.2020.09.026zbMath1475.68282OpenAlexW3087870029MaRDI QIDQ2054049

Edwin Lughofer

Publication date: 30 November 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.09.026

zbMATH Keywords

evolving neuro-fuzzy systemsmulti-innovation RFWLSonline data streamsrecursive correntropyrecursive estimation of consequent parametersrecursive fuzzily weighted least squares (RFWLS)recursive weighted total least squares (RWTLS)

Mathematics Subject Classification ID

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

Related Items (2)

Recursive least mean dual p-power solution to the generalization of evolving fuzzy system under multiple noises ⋮ A new complex evidence theory

Uses Software

• ElemStatLearn
• MOA
• PANFIS

Cites Work

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• Overview of total least-squares methods
• Maximum correntropy Kalman filter
• Evolving fuzzy systems -- methodologies, advanced concepts and applications.
• Comparing parameter choice methods for regularization of ill-posed problems
• On-line incremental feature weighting in evolving fuzzy classifiers
• An incremental support vector machine-trained TS-type fuzzy system for online classification problems
• Machine learning in healthcare informatics
• Discovery science. 9th international conference, DS 2006, Barcelona, Spain, October 7--10, 2006. Proceedings
• Structure identification of fuzzy model
• Triangular norms
• Knowledge Discovery from Data Streams
• Regularized Total Least Squares: Computational Aspects and Error Bounds
• Fuzzy identification of systems and its applications to modeling and control

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