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Comparative Analysis of Symbolic Reasoning Models for Fuzzy Cognitive Maps

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Uncertainty Management with Fuzzy and Rough Sets

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 377))

Abstract

Fuzzy Cognitive Maps (FCMs) can be defined as recurrent neural networks that allow modeling complex systems using concepts and causal relations. While this Soft Computing technique has proven to be a valuable knowledge-based tool for building Decision Support Systems, further improvements related to its transparency are still required. In this paper, we focus on designing an FCM-based model where both the causal weights and concepts’ activation values are described by words like low, medium or high. Hybridizing FCMs and the Computing with Words paradigm leads to cognitive models closer to human reasoning, making it more comprehensible for decision makers. The simulations using a well-known case study related to simulation scenarios illustrate the soundness and potential application of the proposed model.

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References

  1. Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24, 65–75 (1986)

    Article  Google Scholar 

  2. Nápoles, G., Grau, I., Bello, R., Grau, R.: Two-steps learning of fuzzy cognitive maps for prediction and knowledge discovery on the HIV-1 drug resistance. Exp. Syst. Appl. 41(3), 821–830 (2014)

    Article  Google Scholar 

  3. Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)

    Article  Google Scholar 

  4. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems ad decision processes. IEEE Trans. Syst. Man Cybern. SMC-3(1), 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  5. Kosko, B.: Neural Networks and Fuzzy Systems: A Dynamic System Approach to Machine Intelligence. Englewood Cliffs (1992)

    Google Scholar 

  6. Kosko, B.: Hidden patterns in combined and adaptive knowledge networks. Int. J. Approx. Reason. 2(4), 377–393 (1988)

    Article  Google Scholar 

  7. Bonissone, P.P., Decker, K.S.: Selecting uncertainty calculi and granularity: an experiment in trading-off precision and complexity, pp. 217–247. Amsterdam, The Netherlands (1986)

    Google Scholar 

  8. Degani, R., Bortolan, G.: The problem of linguistic approximation in clinical decision making. Int. J. Approx. Reason. 2, 143–162 (1988)

    Article  Google Scholar 

  9. Delgado, M., Verdegay, J.L., Vila, M.A.: On aggregation operations of linguistic labels. Int. J. Intell. Syst 8, 351–370 (1993)

    Article  Google Scholar 

  10. Pérez-Teruel, K., Leyva-Vázquez, M., Espinilla, M.: Computación con palabras en la toma de decisiones mediante mapas cognitivos difusos. Revista Cubana de Ciencias Informáticas 8(2), 19–34 (2014)

    Google Scholar 

  11. Pérez-Teruel, K., Leyva-Vázquez, M., Estrada-Sentí, V.: Mental models consensus process using fuzzy cognitive maps and computing with words. Ing. Univ. 19(1), 173–188 (2015)

    Google Scholar 

  12. Rickard, J.T., Aisbett, J., Yager, R.R.: Computing with words in fuzzy cognitive maps. In: Proceedings of World Conference on Soft Computing, pp. 1–6 (2015)

    Google Scholar 

  13. Dujmovic, J.: Continuous preference logic for system evaluation. IEEE Trans. Fuzzy Syst 15(6), 1082–1099 (2007)

    Article  Google Scholar 

  14. Dujmovic, J., Larsen, H.L.: Generalized conjunction/disjunction. Int. J. Approx. Reason. 46, 423–446 (2007)

    Article  MathSciNet  Google Scholar 

  15. Rickard, J.T., Aisbett, J., Yager, R.R., Gibbon, G.: Fuzzy weighted power means in evaluation decisions. In: 1st World Symposium on Soft Computing (2010)

    Google Scholar 

  16. Rickard, J.T., Aisbett, J., Yager, R.R., Gibbon, G.: Linguistic weighted power means: comparison with the linguistic weighted average. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), pp. 2185–2192 (2011)

    Google Scholar 

  17. Hao, M., Mendel, J.M.: Encoding words into normal interval type-2 fuzzy sets: HM approach. IEEE Trans. Fuzzy Syst. 24, 865–879 (2016)

    Article  Google Scholar 

  18. Rickard, J.T., Aisbett, J., Yager, R.R.: A new fuzzy cognitive map structure based on the weighted power mean. IEEE Trans. Fuzzy Syst. 23, 2188–2202 (2015)

    Article  Google Scholar 

  19. Najafi, A., Amirkhani, A., Papageorgiou, E.I., Mosavi, M.R.: Medical decision making based on fuzzy cognitive map and a generalization linguistic weighted power mean for computing with words (2017)

    Google Scholar 

  20. Gónzalez, M.P., De La Rosa, C.G.B., and Francisco José Cedeña Moran. Fuzzy cognitive maps and computing with words for modeling project portfolio risks interdependencies. Int. J. Innov. Appl. Stud., 15(4):737–742, mayo, 2016

    Google Scholar 

  21. Saleh, S.H., Rivas, S.D.L., Gomez, A.M.M., Mohsen, F.S., Vzquez, M.L.: Representación del conocimiento mediante mapas cognitivos difusos y conjuntos de términos lingüisticos difusos dudosos en la biomedicina. Int. J. Innov. Appl. Stud. 17(1), 312–319 (2016)

    Google Scholar 

  22. Torra, V., Narukawa, Y.: On hesitant fuzzy sets and decision. In: IEEE International Conference, pp. 1378–1382 (2009)

    Google Scholar 

  23. Frias, M., Filiberto, Y., Nápoles, G., Vahoof, K., Bello, R.: Fuzzy cognitive maps reasoning with words: an ordinal approach. In: ISFUROS 2017 (2017)

    Google Scholar 

  24. Frias, M., Filiberto, Y., Nápoles, G., Garcia-Socarras, Y., Vahoof, K., Bello, R.: Fuzzy cognitive maps reasoning with words based on triangular fuzzy numbers. In MICAI 2017 (2017)

    Google Scholar 

  25. Van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of saaty’s priority theory. Fuzzy Sets Syst 11, 229–241 (1983)

    Article  MathSciNet  Google Scholar 

  26. Pacheco, M.A.C., Vellasco, M.M.B.R.: Intelligent Systems in Oil Field Developmnt Under Uncertainty. Springer, Berlin, Heidelberg (2009)

    Book  Google Scholar 

  27. Akther, S.U., Ahmad, T.: A computational method for fuzzy arithmetic operations. Daffodil Int. Univ. J. Sci. Technol. 4(1), 18–22 (2009)

    Article  Google Scholar 

  28. Reznik, L.: Fuzzy Controller Handbook. Newnes (1997)

    Google Scholar 

  29. Weihua, S., Peng, W., Zeng, S., Pen, B., Pand, T.: A method for fuzzy group decision making based on induced aggregation operators and euclidean distance. Int. Trans. Oper. Res. 20, 579–594 (2013)

    Article  MathSciNet  Google Scholar 

  30. Xu, Z.S.: Fuzzy harmonic mean operators. Int. J. Intell. Syst. 24, 152–172 (2009)

    Article  Google Scholar 

  31. Chen, C.T.: Extension of the topsis for group decision-making under fuzzy environment. Fuzzy Sets Syst. 114, 1–9 (2000)

    Article  Google Scholar 

  32. Nápoles, G., Papageorgiou, E., Bello, R., Vanhoof, K.: On the convergence of sigmoid fuzzy cognitive maps. Inf. Sci. 349–350, 154–171 (2016)

    Article  Google Scholar 

  33. Carvalho, J.P.: On the semantics and the use of fuzzy cognitive maps and dynamic cognitive maps in social sciences. Fuzzy Sets Syst. 214, 6–19 (2013)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors would like to thank to John T. Rickard from Distributed Infinity, Inc. Larkspur, CO, USA for his support with the simulations.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Camaguey, Camaguey, Cuba

    Mabel Frias & Yaima Filiberto

  2. Hasselt Universiteit Agoralaan gebouw D, Diepenbeek, Belgium

    Gonzalo Nápoles & Koen Vanhoof

  3. Larus Technologies Corporation, School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Rafael Falcon

  4. Department of Computer Science, University of Las Villas, Santa Clara, Cuba

    Rafael Bello

Authors
  1. Mabel Frias
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  2. Yaima Filiberto
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  3. Gonzalo Nápoles
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  4. Rafael Falcon
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  5. Rafael Bello
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  6. Koen Vanhoof
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Corresponding author

Correspondence to Mabel Frias .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Rafael Falcon

  3. Department of Computer Science and Artificial Intelligence, Technical School of Informatics and Telecommunications Engineering, University of Granada, Granada, Spain

    José Luis Verdegay

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Frias, M., Filiberto, Y., Nápoles, G., Falcon, R., Bello, R., Vanhoof, K. (2019). Comparative Analysis of Symbolic Reasoning Models for Fuzzy Cognitive Maps. In: Bello, R., Falcon, R., Verdegay, J. (eds) Uncertainty Management with Fuzzy and Rough Sets. Studies in Fuzziness and Soft Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-10463-4_7

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