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Information aggregating networks based on extended Sugeno's fuzzy integral

  • Fuzzy — Neural Networks
  • Conference paper
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Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms (WWW 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1011))

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Abstract

Sugeno's fuzzy integral is a functional to aggregate partial evaluations for an object in consideration of importance degrees of evaluation items. This paper presents the issues related to Sugeno's fuzzy integral for information aggregation. For the identification of importance degrees of evaluation items with the properties of fuzzy measures, we suggest to use a genetic algorithm based method. To improve the behavior of the fuzzy integral by avoiding excessive emphasis of pessimistic aspects, we introduce compensatory operators into the fuzzy integral. On the other hand, to tune the parameters for the used compensatory operators and to perform the fuzzy integral in parallel computation, we propose a network model.

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Authors and Affiliations

  1. Dept. Computer Science, KAIST(Korea Advanced Institute of Science and Technology), Taejon, 305-701, Seoul, Korea

    Keon-Myung Lee & Hyung Lee-Kwang

Authors
  1. Keon-Myung Lee
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  2. Hyung Lee-Kwang
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Editor information

Takeshi Furuhashi

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© 1995 Springer-Verlag Berlin Heidelberg

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Lee, KM., Lee-Kwang, H. (1995). Information aggregating networks based on extended Sugeno's fuzzy integral. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_5

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  • DOI: https://doi.org/10.1007/3-540-60607-6_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60607-9

  • Online ISBN: 978-3-540-48457-8

  • eBook Packages: Springer Book Archive

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