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Multi-Criteria Decision Making Based on Fuzzy Measure

  • Sanghyuk Lee
  • Yan Sun
  • Di Feng
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 235)

Abstract

Decision procedure was done with the evaluation of multi-criterion analysis. Importance of each criterion was considered through heuristically method, specially it was based on the heuristic least mean square algorithm. To consider coalition evaluation, it was carried out by calculation of Shapley index and Interaction value. The model output is also analyzed with the help of those two indexes, and the procedure was also displayed with details. Finally, the differences between the model output and the desired results are evaluated thoroughly, several problems are raised at the end of the example which require for further studying.

Keywords

Decision making Heuristic least square Shapley index Interaction index Fuzzy membership function 

References

  1. 1.
    Nash, SG. Sofer A (1996) Linear and nonlinear programming. McGraw-Hill, New YorkGoogle Scholar
  2. 2.
    Grabisch M, Roubens M (2000) Application of the Choquet integral in multi-criteria decision makingGoogle Scholar
  3. 3.
    Sugeno M (1974) Theory of fuzzy integrals and its application. Ph.D. thesis, Tokyo Institute of TechnologyGoogle Scholar
  4. 4.
    Xuecheng L (1992) Entropy, distance measure and similarity measure of fuzzy sets and their relations. Fuzzy Sets Syst 52:305–318MATHCrossRefGoogle Scholar
  5. 5.
    Bhandari D, Pal NR (1993) Some new information measure of fuzzy sets. Inform Sci 67:209–228MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Ghosh A (1995) Use of fuzziness measure in layered networks for object extraction: a generalization. Fuzzy Sets Syst 72:331–348CrossRefGoogle Scholar
  7. 7.
    Lee SH, Pedrycz W, Sohn G (2009) Design of similarity and dissimilarity measures for fuzzy sets on the basis of distance measure. Int J Fuzzy Syst 11:67–72MathSciNetGoogle Scholar
  8. 8.
    Lee SH, Ryu KH, Sohn GY (2009) Study on entropy and similarity measure for fuzzy set. IEICE Trans Inf Syst E92-D:1783–1786Google Scholar
  9. 9.
    Grabisch M (1995) A new algorithm for identifying fuzzy measures and its application to pattern to pattern recognition. In: International joint conference of the 4th IEEE international conference on fuzzy systems and 2nd international fuzzy engineering symposium, pp 145–150Google Scholar
  10. 10.
    Mori T, Murofushi T (1989) An analysis of evaluation model using fuzzy measures and the Choquet integral. In: 5th fuzzy system symposium, Kobe, 2–3 June 1989 (in Japanese)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Electrical and Electronic EngineeringSuzhouChina
  2. 2.School of Business, Economics and ManagementSuzhouChina

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