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Least Squares Method for L-R Fuzzy Variables

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Fuzzy Logic and Applications (WILF 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5571))

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Abstract

The least squares method is used to determine the fuzzy regression. The data for the regression equation are observations for the output and input variables. Analogous assumptions for those used in case of the classical regression are adopted - concerning the fuzzy random component of the model. It is shown how to determine the possibilistic distributions of the output variable and the model coefficients if the random component of the model is an L-R fuzzy variable and its generative probabilistic distribution is known.

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Author information

Authors and Affiliations

  1. Institute of Organization and Management, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Barbara Gładysz & Dorota Kuchta

Authors
  1. Barbara Gładysz
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  2. Dorota Kuchta
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Editor information

Editors and Affiliations

  1. Dipartimento di Matematica e Applicazioni, Università di Palermo, via Archirafi, 34, 90123, Palermo, Italy

    Vito Di Gesù

  2. Center for Soft Computing Research, Machine Intelligence Unit, Indian Statistical Institute,, 203 Barrackpore Trunk Road, 700 108, Kolkata, India

    Sankar Kumar Pal

  3. Dipartimento di Scienze Applicate, Università di Napoli "Parthenope", Via Alcide De Gasperi, 5,, 80133, Napoli, Italy

    Alfredo Petrosino

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© 2009 Springer-Verlag Berlin Heidelberg

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Gładysz, B., Kuchta, D. (2009). Least Squares Method for L-R Fuzzy Variables. In: Di Gesù, V., Pal, S.K., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2009. Lecture Notes in Computer Science(), vol 5571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02282-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-02282-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02281-4

  • Online ISBN: 978-3-642-02282-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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