Evaluation model of humanistic systems by fuzzy multiple integral and fuzzy correlation

  • Eiichiro Tazaki
  • Michio Amagasa
System Modelling And Identification
Part of the Lecture Notes in Computer Science book series (LNCS, volume 27)


We have proposed a mathematical model of subjective evaluation on the basis of fuzzy integral. In particular, we have developed the evaluating method of complex systems composed of several subsystems by virtue of fuzzy multiple integral. Furthermore, for the sake of identifying the preference measure, that is fuzzy measure , effectively, we have introduced the fuzzy correlation.

In order to show how the proposed method works, an example of the subjective evaluation for a class of figures has been illustrated and the consistency of experimental values and computed values has been successfully obtained as shown in Table 10.

Applications to the marginal evaluation problem to meet specification will be a future extension of this approach.


Membership Function Subjective Evaluation Longe Length Preference Measure Fuzzy Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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    L.A. Zadeh: Fuzzy Sets, Information and Control, 8, 338/353 (1965)CrossRefGoogle Scholar
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    M. Sugeno: Fuzzy Measure and Fuzzy Integral, Trans. SICE(Japan), 8, 218/226 (1972)Google Scholar
  3. 3).
    E. Tazaki and M.Amagasa: Evaluation of Complex Systems by Fuzzy Multiple Integral and Fuzzy Correlation, Paper presented to the 16-th Joint Annual Meeting of Automatic Control, Tokyo, Japan (1973)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1975

Authors and Affiliations

  • Eiichiro Tazaki
    • 1
  • Michio Amagasa
    • 1
  1. 1.Faculty of Science and TechnologyScience University of TokyoNoda City, ChibaJapan

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