Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/40292 
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dc.contributor.authorGottschling, Andreasen
dc.contributor.authorKreuter, Christofen
dc.date.accessioned2010-07-27-
dc.date.accessioned2010-09-24T14:30:10Z-
dc.date.available2010-09-24T14:30:10Z-
dc.date.issued1999-
dc.identifier.urihttp://hdl.handle.net/10419/40292-
dc.description.abstractThe 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.en
dc.language.isoengen
dc.publisher|aDeutsche Bank Research |cFrankfurt a. M.en
dc.relation.ispartofseries|aResearch Notes |x99-3en
dc.subject.jelC0en
dc.subject.jelC2en
dc.subject.jelC4en
dc.subject.jelC6en
dc.subject.ddc330en
dc.subject.keywordFuzzy Logicen
dc.subject.keywordNeural Networksen
dc.subject.keywordNonlinear Modelingen
dc.subject.keywordOptimizationen
dc.subject.stwNichtlineare Optimierungen
dc.subject.stwNeuronale Netzeen
dc.subject.stwTheorieen
dc.titleApproximation properties of the neuro-fuzzy minimum function-
dc.typeWorking Paperen
dc.identifier.ppn821922408en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:dbrrns:993en

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