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International Conference Automation

ICA 2017: Automation 2017 pp 554–564Cite as

Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 550))

Abstract

Fuzzy cognitive map (FCM) is a soft computing technique for modeling decision support systems. Construction of the FCM model is based on the selection of concepts important for the analyzed problem and determining significant connections between them. Fuzzy cognitive map can be initialized based on expert knowledge or automatic constructed from data with the use of supervised or evolutionary learning algorithm. FCM models learned from data are much denser than those created by experts. This paper proposes a new evolutionary approach for fuzzy cognitive maps learning based on system performance indicators. The learning process has been carried out with the use of Elite Genetic Algorithm and Individually Directional Evolutionary Algorithm. The developed approach allows to receive FCM model more similar to the reference system than standard methods for fuzzy cognitive maps learning.

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References

  1. Ahmadi, S., Forouzideh, N., Alizadeh, S., Papageorgiou, E.I.: Learning fuzzy cognitive maps using imperialist competitive algorithm. Neural Comput. Appl. 26(6), 1333–1354 (2015)

    Article  Google Scholar 

  2. Borisov, V.V., Kruglov, V.V., Fedulov, A.C.: Fuzzy Models and Networks. Publishing house Telekom, Moscow (2004). (in Russian)

    Google Scholar 

  3. Chi, Y., Liu, J.: Learning of fuzzy cognitive maps with varying densities using multi-objective evolutionary algorithms. IEEE Trans. Fuzzy Syst. 24(1), 71–81 (2016)

    Article  Google Scholar 

  4. Homenda, W., Jastrzebska, A., Pedrycz, W.: Modeling time series with fuzzy cognitive maps. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 2055–2062 (2014)

    Google Scholar 

  5. Jastriebow, A., Poczeta, K.: Analysis of multi-step algorithms for cognitive maps learning. Bull. Pol. Acad. Sci. Tech. Sci. 62(4), 735–741 (2014)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  7. Kosko, B.: Fuzzy Engineering. Prentice-Hall, Englewood Cliffs (1997)

    MATH  Google Scholar 

  8. Kubuś, Ł.: Individually directional evolutionary algorithm for solving global optimization problems - comparative study. Int. J. Intell. Syst. Appl. (IJISA) 7(9), 12–19 (2015)

    Google Scholar 

  9. Kubuś, Ł., Poczeta, K., Yastrebov, A.: A New Learning Approach for Fuzzy Cognitive Maps based on System Performance Indicators. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1398–1404 (2016)

    Google Scholar 

  10. Lee, K.C., Lee, W.J., Kwon, O.B., Han, J.H., Yu, P.I.: Strategic planning simulation based on fuzzy cognitive map knowledge and differential game. Simulation 71(5), 316–327 (1998)

    Article  MATH  Google Scholar 

  11. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, New York (1996)

    Book  MATH  Google Scholar 

  12. Papageorgiou, E.I., Poczeta, K., Laspidou, C.: Application of fuzzy cognitive maps to water demand prediction. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2015)

    Google Scholar 

  13. Poczeta, K., Yastrebov, A., Papageorgiou, E.I.: Learning fuzzy cognitive maps using structure optimization genetic algorithm. In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 547–554 (2015)

    Google Scholar 

  14. Portmann, E., Kaltenrieder, P., Pedrycz, W.: Knowledge representation through graphs. Procedia Comput. Sci. 62, 245–248 (2015)

    Article  Google Scholar 

  15. Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. Appl. Soft Comput. 12, 3704–3710 (2012)

    Article  Google Scholar 

  16. Silov, V.B.: Strategic Decision-Making in a Fuzzy Environment. INPRO-RES, Moscow (1995). (in Russian)

    Google Scholar 

  17. Stach, W., Kurgan, L., Pedrycz, W.: A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets Syst. 161, 2515–2532 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  19. Stach, W., Pedrycz, W., Kurgan, L.A.: Learning of fuzzy cognitive maps using density estimate. Trans. Syst. Man Cybern. Part B 42(3), 900–912 (2012)

    Article  Google Scholar 

  20. Słoń, G.: Application of models of relational fuzzy cognitive maps for prediction of work of complex systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8467, pp. 307–318. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_27

    Chapter  Google Scholar 

  21. Tsadiras, A.K.: Using fuzzy cognitive maps for e-commerce strategic planning. In: Proceedings of 9th Panhellenic Conference on Informatics, Thessaloniki, Greece, pp. 142–151 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Kielce University of Technology, al. Tysiąclecia Państwa Polskiego 7, 25-314, Kielce, Poland

    Katarzyna Poczęta, Łukasz Kubuś & Alexander Yastrebov

  2. Technological Educational Institute (T.E.I.) of Central Greece, 3rd Km Old National Road Lamia-Athens, 35100, Lamia, Greece

    Elpiniki I. Papageorgiou

Authors
  1. Katarzyna Poczęta
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  2. Łukasz Kubuś
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  3. Alexander Yastrebov
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  4. Elpiniki I. Papageorgiou
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Corresponding author

Correspondence to Katarzyna Poczęta .

Editor information

Editors and Affiliations

  1. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Roman Szewczyk

  2. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Cezary Zieliński

  3. Industrial Research Institute for Automation and Measurements PIAP, Warsaw, Poland

    Małgorzata Kaliczyńska

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Poczęta, K., Kubuś, Ł., Yastrebov, A., Papageorgiou, E.I. (2017). Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2017. ICA 2017. Advances in Intelligent Systems and Computing, vol 550. Springer, Cham. https://doi.org/10.1007/978-3-319-54042-9_55

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  • DOI: https://doi.org/10.1007/978-3-319-54042-9_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54041-2

  • Online ISBN: 978-3-319-54042-9

  • eBook Packages: EngineeringEngineering (R0)

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