Top > JACIII > Analysis of New Aggregation Operators: Mean 3Π

single-jc.php

JACIII Vol.11 No.6 pp. 561-569
doi: 10.20965/jaciii.2007.p0561
(2007)

Paper:

Views over last 60 days: 421

Analysis of New Aggregation Operators: Mean 3Π

Andrei Doncescu*,**, Sebastien Regis***, Katsumi Inoue**,
and Richard Emilion****

*LAAS CNRS UPR 8001 Toulouse, France

**National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan

***University of West French Indies Point-a-Pitre, France

****University of Orleans, France

Received:
January 12, 2007
Accepted:
March 20, 2007
Published:
July 20, 2007
Creative Commons License
Keywords:
fusion, mean operators, clustering
Abstract
Knowledge based systems need to deal with aggregation and fusion of data with uncertainty. To use many sources of information in numerical forms for the purpose of decision or conclusion, systems suppose to have tools able to represent the knowledge in a mathematical form. One of the solutions is to use fuzzy logic operators. We present in this article an improvement of the triple Π operator introduced by Yager and Rybalov, which is called mean 3Π. Whereas triple Π is an operator completely reinforced, the presented operator is a mean operator, which makes it more robust to noise.
Cite this article as:
A. Doncescu, S. Regis, K. Inoue, and R. Emilion, “Analysis of New Aggregation Operators: Mean 3Π,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.6, pp. 561-569, 2007.
Data files:
References
  1. [1] I. Bloch, “Information combination operators for data fusion: a comparative fusion with classification,” Technical report, ENST, Paris, 1994. Rapport Technique.
  2. [2] I. Bloch and A. Hunter (Eds.), “Fusion: General concepts and characteristics,” International Journal of Intelligent Systems, 16, pp. 1107-1134, 2001.
  3. [3] T. Calvo, B. De Baets, and J. Fodor, “The functional equations of franck and alsina for uninorms and nullnorms,” Fuzzy Sets and Systems, 120, pp. 385-394, 2001.
  4. [4] D. Dubois and H. Prade, “La fusion d’informations imprécises,” Traitement du Signal, 11(6), pp. 447-458, 1994.
  5. [5] D. Dubois and H. Prade, “On the use of aggregation operations in information fusion process,” Fuzzy Sets and Systems, 142, pp. 143-161, 2004.
  6. [6] D. Dubois, H. Prade, and R. R. Yager (Eds.), “Readings in Fuzzy Sets for Intelligent Systems,” Morgan Kaufman, 1993.
  7. [7] N. Piera-Carreté, J. Aguilar-Martin, and M. Sanchez, “Mixed connectives between min and max,” In 8th Inter. Symp. on Multiple Valued Logic, 1988.
  8. [8] W. Silvert, “Symmetric summation: A class of operations on fuzzy sets,” IEEE Transactions on Systems, Man, Cybernetics, 9(10), pp. 657-659, 1979.
  9. [9] J. Waissman-Vilanova, “Construction d’un modèle comportemental pour la supervision de procédés: application à une station de traitement des eaux,” Ph.D. thesis, LASS - CNRS, November 2000.
  10. [10] R. Yager, “On ordered weighted averaging operators in multicriteria decision making,” IEEE Trans. Syst. Man, Cybern., pp. 183-190, Jan.-Feb. 1988.
  11. [11] R. Yager, “On mean type aggregation,” IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 26(2), pp. 209-221, April 1996.
  12. [12] R. Yager and A. Rybalov, “Uninorm aggregation operators,” Fuzzy Sets and Systems, 80(1), pp. 111-120, 1996.
  13. [13] R. Yager and A. Rybalov, “Full reinforcement operators in aggregation techniques,” IEEE Transactions on Systems, Man, Cybernetics-Part B: Cybernetics, 28(6), 1998.
  14. [14] H. Zimmerman and P. Zynso, “Latent connectives in human decision making,” Fuzzy Sets and Systems, 4, pp. 37-51, 1980.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024