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Regularization of Fuzzy Cognitive Maps for Hybrid Decision Support System

  • Alexey N. Averkin
  • Sergei A. Kaunov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6743)

Abstract

In this paper an aspect of collaborative construction of decision support systems based on fuzzy cognitive maps (FCM) is considered. We propose a way for cooperation in developing process of this systems by different experts and tuning developed systems to given conditions. These goals are attained by employing regularization methods, available since FCM is considered as a neural network. Interpretation and motivation of such approach are described. On the base of fuzzy cognitive map and fuzzy hierarchy model the new approach of Fuzzy Hierarchical Modeling is introduced. Advantages of the method are described. A novel approach to overcoming inherent limitations of Hierarchical Methods by exploiting cognitive maps and multiple distributed information repositories is proposed.

Keywords

Fuzzy Cognitive Map hybrid decision support regularization 

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References

  1. 1.
    Averkin, A.N., Agrafonova, T.V., Titova, N.V.: System of Decision Making Support Based on Fuzzy Models. Journal of Computer and Systems Sciences International 48, 89–100 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Sahbi, H., Boujemaa, N.: Fuzzy Clustering: Consistency of Entropy Regularization. In: International Conference on Computational Intelligence (Special Session on Fuzzy Clustering), Dortmund, Germany (2004)Google Scholar
  3. 3.
    Pajares, G., Guijarro, M., Herrera, P.J., Ruz, J.J., de la Cruz, J.M.: Fuzzy Cognitive Maps Applied to Computer Vision Tasks. In: Glykas, M. (ed.) Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Studies in Fuzziness and Soft Computing, vol. 247, pp. 270–300. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Carlsson, C., Fuller, R.: Adaptive Fuzzy Cognitive Maps for Hyperknowledge Representation in Strategy Formation Process. In: Proceedings of International Panel Conference on Soft and Intelligent Computing, pp. 43–50. Technical University of Budapest (1996)Google Scholar
  5. 5.
    Hansen, L.K., Rasmussen, C.E.: Pruning from Adaptive Regularization. Neural Computation 6(6), 1222–1231 (1994)CrossRefzbMATHGoogle Scholar
  6. 6.
    Goutte, C., Hansen, L.K.: Regularization with a pruning prior. Neural Networks 10(6), 1053–1059 (1997)CrossRefGoogle Scholar
  7. 7.
    Saati, T.: Decision Making: A Method for Analysis of Hierarchies. Radio i Svyaz, Moscow (1993) (In Russian)Google Scholar
  8. 8.
    Makeev, S.P., Shakhnov, I.F.: Arrangement of Objects in Hierarchical Systems. Izv. Akad. Nauk SSSR, Tekh. Kibern.. 3, 29–46 (1991)Google Scholar
  9. 9.
    Kulinich, A.A.: The Methodology of Cognitive Modeling of Complex Ill-Determined Situations. In: Proceedings of 2nd International Conference on Control Problems, Moscow, vol. 2 (2003) (in Russian)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexey N. Averkin
    • 1
  • Sergei A. Kaunov
    • 1
  1. 1.Computer center of RASMoscowRussia

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