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Reducing the number of parameters of a fuzzy system using scaling functions

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Abstract

Learning techniques are tailored for fuzzy systems in order to tune them or even for deriving fuzzy rules from data. However, a compromise between accuracy and interpretability has to be found. Flexible fuzzy systems with a large number of parameters and high degrees of freedom tend to function as black boxes. In this paper, we introduce an interpretation of fuzzy systems that enables us to work with a small number of parameters without loosing flexibility or interpretability. In this way, we can provide a learning algorithm that is efficient and yields accuracy as well as interpretability. Our fuzzy system is based on extremely simple fuzzy sets and transformations using interpretable scaling functions of the input variables.

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Authors and Affiliations

  1. Department of Computer Science, University of Applied Sciences Braunschweig/Wolfenbuettel, Salzdahlumer Str. 46/48, D-38302, Wolfenbuettel, Germany

    Frank Klawonn

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  1. Frank Klawonn
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Correspondence to Frank Klawonn.

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Klawonn, F. Reducing the number of parameters of a fuzzy system using scaling functions. Soft Comput 10, 749–756 (2006). https://doi.org/10.1007/s00500-005-0004-z

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  • DOI: https://doi.org/10.1007/s00500-005-0004-z

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