Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/40292
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dc.contributor.authorGottschling, Andreasen_US
dc.contributor.authorKreuter, Christofen_US
dc.date.accessioned2010-07-27en_US
dc.date.accessioned2010-09-24T14:30:10Z-
dc.date.available2010-09-24T14:30:10Z-
dc.date.issued1999en_US
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_US
dc.language.isoengen_US
dc.publisher|aDeutsche Bank Research |cFrankfurt a. M.en_US
dc.relation.ispartofseries|aResearch notes in economics & statistics |x99-3en_US
dc.subject.jelC0en_US
dc.subject.jelC2en_US
dc.subject.jelC4en_US
dc.subject.jelC6en_US
dc.subject.ddc330en_US
dc.subject.keywordFuzzy Logicen_US
dc.subject.keywordNeural Networksen_US
dc.subject.keywordNonlinear Modelingen_US
dc.subject.keywordOptimizationen_US
dc.subject.stwNichtlineare Optimierungen_US
dc.subject.stwNeuronale Netzeen_US
dc.subject.stwTheorieen_US
dc.titleApproximation properties of the neuro-fuzzy minimum functionen_US
dc.type|aWorking Paperen_US
dc.identifier.ppn821922408en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
dc.identifier.repecRePEc:zbw:dbrrns:993-

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