Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/40292
Authors: 
Gottschling, Andreas
Kreuter, Christof
Year of Publication: 
1999
Series/Report no.: 
Research notes in economics & statistics 99-3
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.
Subjects: 
Fuzzy Logic
Neural Networks
Nonlinear Modeling
Optimization
JEL: 
C0
C2
C4
C6
Document Type: 
Working Paper

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