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Using artificial neural networks to aid decision making processes

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Artificial Neural Networks (IWANN 1991)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 540))

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Abstract

Ranking fuzzy numbers is very necessary when we have to make a decision with imprecise information. The comparison depends on decision-maker's subjectivity and then capturing it into algorithms is difficult. Several methods has been developed in order to ranking fuzzy numbers, each of them being subjective, but the lack of real fitness is always present.

Artificial Neural Networks (ANN) are able to model systems with unknown performance (learning their behavior) and thus ANN may be used in Decision Making Problems to disclose decision maker's unknown behavior.

In this paper, we propose ranking fuzzy numbers using ANNs. We present several experiences: We have simulated an ANN that use the Backpropagation algorithm for learning. Also we show that it is possible to take a decision using ANNs, when we have fuzzy information.

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References

  1. ACKLEY, D.H.-HINTON, G.E.-SEJNOWSKI, T.J. (1985). A Learning Algorithm for Boltzmann Machines. Cognitive Science vol 9. pag 147–169

    Google Scholar 

  2. ANDERSON,J.A.-ROSENFELD,E.(Ed.). Neurocomputing. Foundations of Research. MIT Press. MA

    Google Scholar 

  3. BORTOLAN, G.-DEGANI, R. (1985): A review of some methods for ranking fuzzy subsets. Fuzzy Sets and Systems 15, 1–19.

    Google Scholar 

  4. CAMPOS, L.-GONZALEZ, A.: Further contributions to the study of the average value for ranking fuzzy numbers. Int. J. Approx. Reasoning.

    Google Scholar 

  5. CAMPOS, L.M. de-GONZALEZ, A. (1989): A subjective approach for ranking fuzzy numbers. Fuzzy Sets and Systems 29, 145–153.

    Google Scholar 

  6. CANO, J.E.-DELGADO, M.-REQUENA, I. Comparación de números difusos mediantE R.N.A.. Actas I Congreso Español sobre Technologias y Logica Fuzzy. pg. 109–113

    Google Scholar 

  7. DELGADO, M.-VERDEGAY, J.L.-VILA, M.A. (1988): A procedure for ranking fuzzy numbers using fuzzy relations. Fuzzy Sets and Systems 26, 49–62.

    Google Scholar 

  8. DUBOIS, D.-PRADE, H. (1980): Fuzzy Sets and Systems. Theory and Applications. Academic Press. New York

    Google Scholar 

  9. GONZALEZ, A. (1987): Métodos subjectivos para la comparación de números difusos. Tesis Doctoral, Universidad de Granada.

    Google Scholar 

  10. KOHONEN, T. (1984). Self-organitation and Associative Memory. Springer V. Berlin

    Google Scholar 

  11. REQUEN'A, I. (1991). Redes Neuronales Artificiales: Algunas Ideas Básicas. Algunas Cuestiones sobre Tratamiento de la Información en Inteligencia Artificial. pg 110–130. Publicaciones de Universidad de Granada.

    Google Scholar 

  12. RUMELHART, D.-HINTON, G.-WILLIAMS, R. (1986). Learning Representations by Backpropagation Errors. NATURE. 323. Pag 533–536.

    Google Scholar 

  13. SIMPSON, P. (1990). Artificial Neural Systems. Foundations, Paradigms, Applications and implementations. Pergamon Pres. New York.

    Google Scholar 

  14. SZU, H. (1986). Fast Simulated Annealing. AIP Confer. Proc. 151. pg 420–425.

    Google Scholar 

  15. WASSERMAN, P.D.(1989). Neural Computing: Theory and practice. Van Nostrand-New Y.

    Google Scholar 

  16. YAGER, R.R. (1981): A procedure for ordering fuzzy subsets of the unit interval. Inform. Sci. 24, 143–161.

    Google Scholar 

  17. ZADEH, L. A. (1965) Fuzzy Sets. Information and Control. 8 pag 338–353.

    Google Scholar 

  18. ZADEH, L. A. (1975): The concept of a linguistic variable and its application to approximate reasoning. Infor. Scien., 8, 199 (I); 8, 301 (II); 9, 43 (III).

    Google Scholar 

  19. Abstracts Third Int. Conference Information Processing and Management of Uncertainty in Knowledge-based Systems. Paris 1990.

    Google Scholar 

  20. IFSA (1989). Proc. Third IFSA Congress. Seattle (USA). 1989.

    Google Scholar 

  21. IIZUKA'88 (1988). Internat. Workshop on Fuzzy System Applications. Iizuka (Japan).

    Google Scholar 

  22. IIZUKA'90. Proc. Int. Conference on Fuzzy Logic & Neural Networks (IFSA). Japan.

    Google Scholar 

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Authors and Affiliations

  1. Dpto. de Ciencias de la Computación e Inteligencia Artificial de la, Universidad de Granada. Facultad de Ciencias, 18071, Granada

    José E. Cano, Miguel Delgado & Ignacio Requena

Authors
  1. José E. Cano
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  2. Miguel Delgado
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  3. Ignacio Requena
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Editor information

Alberto Prieto

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© 1991 Springer-Verlag Berlin Heidelberg

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Cano, J.E., Delgado, M., Requena, I. (1991). Using artificial neural networks to aid decision making processes. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035928

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  • DOI: https://doi.org/10.1007/BFb0035928

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54537-8

  • Online ISBN: 978-3-540-38460-1

  • eBook Packages: Springer Book Archive

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