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Extracting Interpretable Fuzzy Rules from RBF Networks

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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.

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Authors and Affiliations

  1. Honda Research Institute Europe, 63073, Offenbach/Main, Germany

    Yaochu Jin & Bernhard Sendhoff

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  1. Yaochu Jin
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  2. Bernhard Sendhoff
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Jin, Y., Sendhoff, B. Extracting Interpretable Fuzzy Rules from RBF Networks. Neural Processing Letters 17, 149–164 (2003). https://doi.org/10.1023/A:1023642126478

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