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Concept by Concept Learning of Fuzzy Cognitive Maps

  • M. Furkan Dodurka
  • Engin Yesil
  • Cihan Ozturk
  • Ahmet Sakalli
  • Cagri Guzay
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 412)

Abstract

Fuzzy cognitive maps (FCM) are fuzzy signed directed graphs with feedbacks; they are simple and powerful tool for simulation and analysis of complex, nonlinear dynamic systems. However, FCM models are created by human experts mostly, and so built FCM models are subjective and building a FCM model becomes harder as number of variables increases. So in the last decade several methods are proposed providing automated generation of fuzzy cognitive maps from data. The main drawback of the proposed automated methods is their weaknesses on handling with large number of variables. The proposed method brings out a new strategy called concept by concepts approach (CbC) approach for learning of FCM. It enables the generation of large sized FCM models with a high precision and in a rapid way using the historical data.

Keywords

Fuzzy cognitive maps learning density global optimization 

References

  1. 1.
    Axelrod, R.: Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)Google Scholar
  2. 2.
    Kosko, B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24, 65–75 (1986)CrossRefzbMATHGoogle Scholar
  3. 3.
    Aguilar, J.: A survey about fuzzy cognitive maps papers. International Journal of Computational Cognition 3(2), 27–33 (2005)Google Scholar
  4. 4.
    Papageorgiou, E.I.: Review study on fuzzy cognitive maps and their applications during the last decade. In: 2011 IEEE International Conference on Fuzzy Systems, pp. 828–835. IEEE Computer Society, Taipei (2011)CrossRefGoogle Scholar
  5. 5.
    Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps: A review study. IEEE Trans. Syst., Man Cybern. C Appl. Rev. 42(2), 150–163 (2011)CrossRefGoogle Scholar
  6. 6.
    Papageorgiou, E.I., Salmeron, J.L.: A Review of Fuzzy Cognitive Map research during the last decade. IEEE Transactions on Fuzzy Systems 21(1), 66–79 (2013)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, J.L., Aguilar, L.T., Castillo, O.: A cognitive map and fuzzy inference engine model for online design and self fine-tuning of fuzzy logic controllers. International Journal of Intelligent Systems 24(11), 1134–1173 (2009)CrossRefzbMATHGoogle Scholar
  8. 8.
    Andreou, A.S., Mateou, N.H., Zombanakis, G.A.: Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps. Soft Computing Journal 9(3), 194–210 (2006)CrossRefGoogle Scholar
  9. 9.
    Glykas, M.: Fuzzy cognitive strategic maps in business process performance measurement. Expert Systems with Applications 40(1), 1–14 (2013)CrossRefGoogle Scholar
  10. 10.
    Papageorgiou, E.I., Froelich, W.: Application of Evolutionary Fuzzy Cognitive Maps for Prediction of Pulmonary Infections. IEEE Transactions on Information Technology in Biomedicine 16(1), 143–149 (2012)CrossRefGoogle Scholar
  11. 11.
    Motlagh, O., Tang, S.H., Ismail, N., Ramli, A.R.: An expert fuzzy cognitive map for reactive navigation of mobile robots. Fuzzy Sets and Systems 201, 105–121 (2012)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Acampora, G., Loia, V.: On the Temporal Granularity in Fuzzy Cognitive Maps. IEEE Transactions Fuzzy Systems 19(6), 1040–1057 (2011)CrossRefGoogle Scholar
  13. 13.
    Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A.: Soft Computing Technique of Fuzzy Cognitive Maps to Connect Yield Defining Parameters with Yield in Cotton Crop Production in Central Greece as a Basis for a Decision Support System for Precision Agriculture Application. In: Glykas, M. (ed.) Fuzzy Cognitive Maps. STUDFUZZ, vol. 247, pp. 325–362. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Lee, K.C., Lee, S.: A causal knowledge-based expert system for planning an Internet-based stock trading system. Expert Systems With Applications 39(10), 8626–8635 (2012)CrossRefGoogle Scholar
  15. 15.
    Alizadeh, S., Ghazanfari, M.: Learning FCM by chaotic simulated annealing. Chaos, Solutions & Fractals 41(3), 1182–1190 (2009)CrossRefGoogle Scholar
  16. 16.
    Dickerson, J.A., Kosko, B.: Fuzzy virtual worlds. Artif. Intell. Expert 7, 25–31 (1994)Google Scholar
  17. 17.
    Vazquez, A.: A balanced differential learning algorithm in fuzzy cognitive maps. Technical report, Departament de Llenguatges I Sistemes Informatics, Universitat Politecnica de Catalunya, UPC (2002)Google Scholar
  18. 18.
    Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Fuzzy cognitive map learning based on nonlinear Hebbian rule. In: Australian Conference on Artificial Intelligence, pp. 256–268 (2003)Google Scholar
  19. 19.
    Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approx. Reason. 37(3), 219–249 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Stach, W., Kurgan, L., Pedrycz, W.: Data driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: Proc. IEEE World Congr. Comput. Intell., pp. 1975–1981 (2008)Google Scholar
  21. 21.
    Konar, A., Chakraborty, U.K.: Reasoning and unsupervised learning in a fuzzy cognitive map. Inf. Sci. 170, 419–441 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Parsopoulos, K.E., Papageorgiou, E.I., Groumpos, P.P., Vrahatis, M.N.: A first study of fuzzy cognitive maps learning using particle swarm optimization. In: Proc. IEEE Congr. Evol. Comput., pp. 1440–1447 (2003)Google Scholar
  23. 23.
    Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior. In: Proc. IEEE Congr. Evol. Comput., pp. 364–371 (2001)Google Scholar
  24. 24.
    Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Alizadeh, S., Ghazanfari, M., Jafari, M., Hooshmand, S.: Learning FCM by tabu search. Int. J. Comput. Sci. 2(2), 142–149 (2007)Google Scholar
  26. 26.
    Lin, C., Chen, K., He, Y.: Learning fuzzy cognitive map based on immune algorithm. WSEAS Trans. Syst. 6(3), 582–588 (2007)Google Scholar
  27. 27.
    Yesil, E., Urbas, L.: Big Bang - Big Crunch Learning Method for Fuzzy Cognitive Maps. In: International Conference on Control, Automation and Systems Engineering (2010)Google Scholar
  28. 28.
    Baykasoglu, A., Durmusoglu, Z.D.U., Kaplanoglu, V.: Training fuzzy cognitive maps via extended great deluge algorithm with applications. Comput. Ind. 62(2), 187–195 (2011)CrossRefGoogle Scholar
  29. 29.
    Yesil, E., Ozturk, C., Dodurka, M.F., Sakalli, A.: Fuzzy Cognitive Maps Learning Using Artificial Bee Colony Optimization. In: IEEE Int. Conf. Fuzzy Systems (2013)Google Scholar
  30. 30.
    Khan, M.S., Chong, A.: Fuzzy cognitive map analysis with genetic algorithm. In: Ind. Int. Conf. Artif. Intell. (2003)Google Scholar
  31. 31.
    Yesil, E., Dodurka, M.F.: Goal-Oriented Decision Support using Big Bang-Big Crunch Learning Based Fuzzy Cognitive Map: An ERP Management Case Study. In: IEEE Int. Conf. Fuzzy Systems (2013)Google Scholar
  32. 32.
    Papageorgiou, E.I., Groumpos, P.P.: A new hybrid learning algorithm for fuzzy cognitive maps learning. Appl. Soft Comput. 5, 409–431 (2005)CrossRefGoogle Scholar
  33. 33.
    Zhu, Y., Zhang, W.: An integrated framework for learning fuzzy cognitive map using RCGA and NHL algorithm. In: Int. Conf. Wireless Commun., Netw. Mobile Comput. (2008)Google Scholar
  34. 34.
    Stach, W., Kurgan, L., Pedrycz, W.: A survey of fuzzy cognitive map learning methods. In: Grzegorzewski, P., Krawczak, M., Zadrozny, S. (eds.) Issues in Soft Computing: Theory and Applications, Exit, pp. 71–84 (2005)Google Scholar
  35. 35.
    Stach, W., Kurgan, L., Pedrycz, W.: A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets Syst. 161(19), 2515–2532 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Banini, G.A., Bearman, R.A.: Application of fuzzy cognitive maps to factors affecting slurry rheology. International Journal of Mineral Processing 52, 233–244 (1998)CrossRefGoogle Scholar
  37. 37.
    Erol, O.K., Eksin, I.: A new optimization method: Big Bang-Big Crunch. Advances in Engineering Software 37, 106–111 (2006)CrossRefGoogle Scholar
  38. 38.
    Yesil, E., Urbas, L., Demirsoy, A.: FCM-GUI: A graphical user interface for Big Bang-Big Crunch Learning of FCM. In: Papageorgiou, E. (ed.) Fuzzy Cognitive Maps for Applied Sciences and Engineering – From Fundamentals to Extensions and Learning Algorithms. Intelligent Systems Reference Library. Springer (2013)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • M. Furkan Dodurka
    • 1
    • 2
  • Engin Yesil
    • 1
  • Cihan Ozturk
    • 1
  • Ahmet Sakalli
    • 1
  • Cagri Guzay
    • 1
  1. 1.Faculty of Electrical and Electronics Engineering, Control Engineering DepartmentIstanbul Technical UniversityIstanbulTurkey
  2. 2.GETRON Bilişim Hizmetleri A. Ş.Yıldız Teknik Üniversitesi Davutpaşa KampüsüIstanbulTurkey

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