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Neural Processing Letters

, Volume 17, Issue 2, pp 149–164 | Cite as

Extracting Interpretable Fuzzy Rules from RBF Networks

  • Yaochu Jin
  • Bernhard Sendhoff
Article

Abstract

Radial basis function networks and fuzzy rule systems are functionally equivalent under some mild conditions. Therefore, the learning algorithms developed in the field of artificial neural networks can be used to adapt the parameters of fuzzy systems. Unfortunately, after the neural network learning, the structure of the original fuzzy system is changed and interpretability, which is considered to be one of the most important features of fuzzy systems, is usually impaired. This Letter discusses the differences between RBF networks and interpretable fuzzy systems. Based on these discussions, a method for extracting interpretable fuzzy rules from RBF networks is suggested. Simulation examples are given to embody the idea of this paper.

Keywords

Neural Network Artificial Intelligence Basis Function Complex System Artificial Neural Network 
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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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Yaochu Jin
    • 1
  • Bernhard Sendhoff
    • 1
  1. 1.Honda Research Institute EuropeOffenbach/MainGermany

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