Approximation analysis of feedforward regular fuzzy neural network with two hidden layers

DOI10.1016/J.FSS.2004.02.013zbMath1087.41018OpenAlexW2095067387MaRDI QIDQ1770735

Puyin Liu, Hong-Xing Li

Publication date: 7 April 2005

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

Full work available at URL: https://doi.org/10.1016/j.fss.2004.02.013

zbMATH Keywords

Universal approximationClosure fuzzy mappingFuzzy-valued Bernstein polynomialRegular fuzzy neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Theory of fuzzy sets, etc. (03E72) Approximation by other special function classes (41A30)

Related Items (6)

Evolution computation based learning algorithms of polygonal fuzzy neural networks ⋮ Approximation capabilities of multilayer fuzzy neural networks on the set of fuzzy-valued functions ⋮ Approximation of fuzzy-valued functions by regular fuzzy neural networks and the accuracy analysis ⋮ Approximation of fuzzy functions by regular fuzzy neural networks ⋮ Fuzzy hyperbolic neural network with time-varying delays ⋮ Approximation of fuzzy neural networks based on Choquet integral

Cites Work

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• A note on the extension principle for fuzzy sets
• The fuzzy neural network approximation lemma
• Fuzzy input-output controllers are universal approximators
• Analyses of regular fuzzy neural networks for approximation capabilities
• Can fuzzy neural nets approximate continuous fuzzy functions?
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Universal approximations of continuous fuzzy-valued functions by multi-layer regular fuzzy neural networks

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