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Neural Networks and Soft Computing pp 310–315Cite as

The Newton Interpolation Method with Fuzzy Numbers as the Entries

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Part of the book series: Advances in Soft Computing ((AINSC,volume 19))

Summary

It is typical of some medical experiments, leading to measures regarded as the coordinates of points in the plane, that imprecise data can occur. In spite of it we still would like to derive a formula of the function that interpolates these points. We thus test the Newton interpolation method with divided differences when supposing that the entries will be fuzzy numbers in the L-R form. The equation describing the fuzzy function, which goes through the points, can be used as a prognosis in the case of other points that have only one coordinate known.

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References

  1. Dubois D., Prade H. (1978), Fuzzy Real Algebra. Some Results, Fuzzy Sets and Systems 2, 327–348

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Author information

Authors and Affiliations

  1. Department of Health, Science and Mathematics, Blekinge Institute of Technology, S-37179, Karlskrona, Sweden

    Elisabeth Rakus-Andersson

Authors
  1. Elisabeth Rakus-Andersson
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Editors and Affiliations

  1. Department of Computer Engineering, Technical University Częstochowa, Al. Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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© 2003 Springer-Verlag Berlin Heidelberg

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Rakus-Andersson, E. (2003). The Newton Interpolation Method with Fuzzy Numbers as the Entries. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_45

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  • DOI: https://doi.org/10.1007/978-3-7908-1902-1_45

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0005-0

  • Online ISBN: 978-3-7908-1902-1

  • eBook Packages: Springer Book Archive

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