Approximation properties of the neuro-fuzzy minimum function

Andreas Gottschling and Christof Kreuter

No 99-3, Research Notes from Deutsche Bank Research

Abstract: 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.

Keywords: Fuzzy Logic; Neural Networks; Nonlinear Modeling; Optimization (search for similar items in EconPapers)
JEL-codes: C0 C2 C4 C6 (search for similar items in EconPapers)
Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:dbrrns:993

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