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Comparison of Different Algorithms of Approximation by Extensional Fuzzy Subsets

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Aggregation Functions in Theory and in Practise

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 228))

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Abstract

How to approximate an arbitrary fuzzy subset by an adequate extensional one is a key question within the theory of Extensional Fuzzy Subsets. In a recent paper by the authors [19] different methods were provided to find good approximations. In this work these methods are compared in order to understand better the performance and improvement they give.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, ETS Arquitectura del Valles, Universitat Politècnica de Catalunya, C/ Pere Serra 1-15, 08190, Sant Cugat del Vallès, Spain

    Gabriel Mattioli & Jordi Recasens

Authors
  1. Gabriel Mattioli
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  2. Jordi Recasens
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Corresponding author

Correspondence to Gabriel Mattioli .

Editor information

Editors and Affiliations

  1. Department of Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    Humberto Bustince

  2. Department of Automatica y Computacion, Universidad Publica de Navarra, Pamplona, Spain

    Javier Fernandez

  3. Department of Mathematics and, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  4. Department of Computer Science, Universidad de Alcalá de Henares, Madrid, Spain

    Tomasa Calvo

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Mattioli, G., Recasens, J. (2013). Comparison of Different Algorithms of Approximation by Extensional Fuzzy Subsets. In: Bustince, H., Fernandez, J., Mesiar, R., Calvo, T. (eds) Aggregation Functions in Theory and in Practise. Advances in Intelligent Systems and Computing, vol 228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39165-1_31

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  • DOI: https://doi.org/10.1007/978-3-642-39165-1_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39164-4

  • Online ISBN: 978-3-642-39165-1

  • eBook Packages: EngineeringEngineering (R0)

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