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International Conference on Artificial Neural Networks

ICANN 2009: Artificial Neural Networks – ICANN 2009 pp 598–607Cite as

Height Defuzzification Method on L  ∞  Space

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5768))

Abstract

The mathematical framework for studying of a fuzzy approximate reasoning is presented in this paper. One of the defuzzification methods besides the center of gravity method which is the best well known defuzzification method are described. The continuity of the defuzzification methods and its application to a fuzzy feedback control are discussed.

The paper was supported in part by Grant-in-Aid for Young Scientists (B) #19700225 from Japan Society for the Promotion of Science (JSPS).

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Author information

Authors and Affiliations

  1. University of Marketing and Distribution Sciences, Kobe, 651-2188, Japan

    Takashi Mitsuishi

  2. Shinshu University, Nagano, 380-8553, Japan

    Yasunari Shidama

Authors
  1. Takashi Mitsuishi
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  2. Yasunari Shidama
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Piazza L. da Vinci 32, 20133, Milano, Italy

    Cesare Alippi

  2. Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Street, 1678, Nicosia, Cyprus

    Marios Polycarpou , Christos Panayiotou  & Georgios Ellinas ,  & 

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Mitsuishi, T., Shidama, Y. (2009). Height Defuzzification Method on L  ∞  Space. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_62

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  • DOI: https://doi.org/10.1007/978-3-642-04274-4_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04273-7

  • Online ISBN: 978-3-642-04274-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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