Approximation properties of the neuro-fuzzy minimum function

Andreas Gottschling and Christof Kreuter

The integration of fuzzy logic systems and neural networks in data driven nonlinear modeling applications has generally been limited to functions based upon the multiplicative fuzzy implication rule for theoretical and computational reasons. We derive a universal approximation result for the minimum fuzzy implication rule as well as a differentiable substitute function that allows fast optimization and function approximation with neuro-fuzzy networks.

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Year of publication:	1999
Authors:	Gottschling, Andreas ; Kreuter, Christof
Publisher:	Frankfurt a. M. : Deutsche Bank Research
Subject:	Fuzzy Logic \| Neural Networks \| Nonlinear Modeling \| Optimization \| Nichtlineare Optimierung \| Nonlinear programming \| Neuronale Netze \| Neural networks \| Theorie \| Theory

Extent:	Online-Ressource ([7] S.) graph. Darst.
Series:	Research notes in economics & statistics. - Frankfurt, M. : DB Research, ISSN 1616-0428, ZDB-ID 2814336-X. - Vol. 99-3
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper
Language:	English
Notes:	Systemvoraussetzungen: Acrobat Reader
Other identifiers:	hdl:10419/40292 [Handle]
Classification:	C0 - Mathematical and Quantitative Methods. General ; C2 - Econometric Methods: Single Equation Models ; C4 - Econometric and Statistical Methods: Special Topics ; C6 - Mathematical Methods and Programming
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010504316