Data Driven Fuzzy Modelling with Neural Networks

  • C. Moraga
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 179)

Abstract

Extraction of models for complex systems from numerical data of behavior is studied. In particular, systems representable as sets of fuzzy if-then rules where the premises are not connected by t-norms, but by a compensating aggregation operator are discussed. A method is presented to extract this kind of fuzzy rules with support of neural networks. Finally it is shown that it is possible to extract compensating fuzzy if-then rules from a great number of already existing feedforward neural networks.

Keywords

Neural Network Hide Layer Fuzzy Logic Fuzzy Rule Fuzzy Inference System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • C. Moraga
    • 1
  1. 1.Dept. Computer Science and Computer EngineeringUniversity of DortmundGermany

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