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Hybrid Fuzzy Least-Squares Regression Model for Qualitative Characteristics

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Integrated Uncertainty Management and Applications

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 68))

Abstract

A method for hybrid fuzzy least-squares regression is developed in this paper. Input and output information is presented in the form of linguistic meanings of qualitative characteristics. A method of formalization of these meanings as (L - R) fuzzy numbers is developed by the authors. The method of regression’s creation is based on the transformation of the input and output fuzzy numbers into intervals, which are called weighted intervals. The proposed method extends a group of initial data membership functions as it can be applied not only to normalized triangular fuzzy numbers, but also to (L-R) fuzzy numbers. The numerical example has demonstrated that the developed hybrid regression model can be used for analysis of relations among qualitative characteristics with success.

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Author information

Authors and Affiliations

  1. Department of Electronics and Computers, Moscow State Forest University, Russia

    O. Poleshchuk & E. Komarov

Authors
  1. O. Poleshchuk
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  2. E. Komarov
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Editor information

Editors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, 923-1292, Ishikawa, Japan

    Van-Nam Huynh  & Yoshiteru Nakamori  & 

  2. Department of Engineering Mathematics, University of Bristol, Queen’s Building, University Walk, BS8 1TR, Bristol, United Kingdom

    Jonathan Lawry

  3. Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, 560-8531, Osaka, Japan

    Masahiro Inuiguchi

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Poleshchuk, O., Komarov, E. (2010). Hybrid Fuzzy Least-Squares Regression Model for Qualitative Characteristics. In: Huynh, VN., Nakamori, Y., Lawry, J., Inuiguchi, M. (eds) Integrated Uncertainty Management and Applications. Advances in Intelligent and Soft Computing, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11960-6_18

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  • DOI: https://doi.org/10.1007/978-3-642-11960-6_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11959-0

  • Online ISBN: 978-3-642-11960-6

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