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New Results on Fuzzy Regression by Using Genetic Programming

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Book cover Genetic Programming (EuroGP 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2278))

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Abstract

In this paper we continue the work on symbolic fuzzy regression problems. That means that we are interested in finding a fuzzy function f, which best matches given data pairs (X i,Y i ) 1 ≤i ≤k of fuzzy numbers. We use a genetic programming approach for finding a suitable fuzzy function and will present test results about linear, quadratic and cubic fuzzy functions.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Siegen, Hölderlinstr. 3, 57068, Siegen, Germany

    Wolfgang Golubski

Authors
  1. Wolfgang Golubski
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Idaho, 83844-1010, Moscow, ID, USA

    James A. Foster

  2. INRIA Rocquencourt, B.P. 105, 78153, Le Chesnay Cedex, France

    Evelyne Lutton

  3. School of Computer Science, University of Birmingham, B15 2TT, Birmingham, UK

    Julian Miller

  4. Department of Computer Science and Information Systems, University of Limerick, Schuman Building, Limerick, Ireland

    Conor Ryan

  5. Dip. Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema, Italy

    Andrea Tettamanzi

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© 2002 Springer-Verlag Berlin Heidelberg

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Golubski, W. (2002). New Results on Fuzzy Regression by Using Genetic Programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A. (eds) Genetic Programming. EuroGP 2002. Lecture Notes in Computer Science, vol 2278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45984-7_30

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  • DOI: https://doi.org/10.1007/3-540-45984-7_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43378-1

  • Online ISBN: 978-3-540-45984-2

  • eBook Packages: Springer Book Archive

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