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Generalised Fuzzy Aggregation Operators

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Machine Learning and Data Mining in Pattern Recognition (MLDM 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1715))

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Abstract

Fuzzy logic offers the option to try to model the non-linearity of the functioning of the human brain when several pieces of evidence are combined to make an inference. In the proposed scheme a Fuzzy reasoning system includes a training stage during which the most appropriate aggregation operators are selected. To allow for different importance to be given to different pieces of evidence, the Fuzzy membership functions used are allowed to take values in a range [0,w], with w ≠ 1. Then the need arises for the generalization of the aggregetion operators to cope with such membership functions. In this paper we examine the properties of such generalised operators, that make them appropriate for use in Fuzzy reasoning.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guildford, GU2 5XH, UK

    M. Petrou & K. R. Sasikala

Authors
  1. M. Petrou
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  2. K. R. Sasikala
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Editor information

Editors and Affiliations

  1. Institut für Bildverarbeitung und angewandte Informatik, Arno-Nitzsche-Str. 45, D-04277, Leipzig, Germany

    Petra Perner

  2. School of Electronic Engineering, Information Technology and Mathematics, University of Surrey, Guilford, GU2 5XH, UK

    Maria Petrou

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© 1999 Springer-Verlag Berlin Heidelberg

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Petrou, M., Sasikala, K.R. (1999). Generalised Fuzzy Aggregation Operators. In: Perner, P., Petrou, M. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 1999. Lecture Notes in Computer Science(), vol 1715. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48097-8_16

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  • DOI: https://doi.org/10.1007/3-540-48097-8_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66599-1

  • Online ISBN: 978-3-540-48097-6

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