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Approximation properties of the neuro-fuzzy minimum function

Author

Listed:
  • Gottschling, Andreas
  • Kreuter, Christof

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.

Suggested Citation

  • Gottschling, Andreas & Kreuter, Christof, 1999. "Approximation properties of the neuro-fuzzy minimum function," Research Notes 99-3, Deutsche Bank Research.
    • Handle: RePEc:zbw:dbrrns:993
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    More about this item

    Keywords

    Fuzzy Logic; Neural Networks; Nonlinear Modeling; Optimization;
    All these keywords.

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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