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Methods and Algorithms for Fuzzy Cognitive Map-based Modeling

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Fuzzy Cognitive Maps for Applied Sciences and Engineering

Abstract

The challenging problem of complex systems modeling methods with learning capabilities and characteristics that utilize existence knowledge and human experience is investigated using Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. Their usefulness has been proved from their wide applicability in diverse domains. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and ease of use. In general, they model the behavior of a complex system utilizing experts knowledge and/or available knowledge from existing databases. They are mainly used for knowledge representation and decision support where their modeling features and their learning capabilities make them efficient to support these tasks. This chapter gathers the methods and learning algorithms of FCMs applied to modeling and decision making tasks. A comprehensive survey of the current modeling methodologies and learning algorithms of FCMs is presented. The leading methods and learning algorithms, concentrated on modeling, are described analytically and analyzed presenting experimental results of a known case study. The main features of computational methodologies are compared and future research directions are outlined.

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References

  1. Acampora, G., Loia, V.: On the temporal granularity in fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 19(6), 1040–1057 (2011)

    Article  Google Scholar 

  2. Aguilar, J.: A dynamic fuzzy cognitive map approach based on random neural networks. Int. J. Comput. Cogn. 1(4), 91–107 (2003)

    Google Scholar 

  3. Aguilar, J., Contreras, J.: The FCM designer tool in fuzzy cognitive maps. Stud. Fuzziness Soft Comput. 247, 71–87 (2010)

    Article  Google Scholar 

  4. Alizadeh, S., Ghazanfari, M.: Learning FCM by chaotic simulated annealing. Chaos, Solitons Fractals 41, 1182–1190 (2008)

    Article  Google Scholar 

  5. Alizadeh, S., Ghazanfari, M., Fathian, M.: Using data mining for learning and clustering FCM. Int. J. Comput. Intell. 4(2), 118–125 (2008)

    Google Scholar 

  6. Alizadeh, S., Ghazanfari, M., Jafari, M., Hooshmand, S.: Learning FCM by Tabu search. Int. J. Comput. Sci. 2, 143–149 (2008)

    Google Scholar 

  7. Alter, S.L.: Decision Support Systems: Current Practice and Continuing Challenge. Addison Wesley, Reading (1980)

    Google Scholar 

  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 Comput. 9(3), 194–210 (2005)

    Article  Google Scholar 

  9. Arthi, K., Tamilarasi, A., Papageorgiou, E.I.: Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Syst. Appl. 38, 1282–1292 (2011)

    Article  Google Scholar 

  10. Axelrod, R.: Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)

    Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11(1), 1014–1020 (2010)

    Article  Google Scholar 

  13. Boutalis, Y., Kottas, T., Christodoulou, M.: Estimation, adaptive of fuzzy cognitive maps with proven stability and parameter convergence. IEEE Trans. Fuzzy Syst. 17(4), 874–889 (2009)

    Article  Google Scholar 

  14. Cai, Y., Miao, C., Tan, A.H., Shen, Z., Li, B.: Creating an immersive game world with evolutionary fuzzy cognitive maps. IEEE J. Comput. Graph. Appl. 30(2), 58–70 (2010)

    Article  Google Scholar 

  15. Carvalho, J.P.: Rule based fuzzy cognitive maps in humanities, social sciences and economics. Stud. Fuzziness Soft Comput. 273, 289–300 (2012)

    Article  Google Scholar 

  16. Carvalho, J.P., Tome, J.A.: Rule based fuzzy cognitive maps–expressing time in qualitative system dynamics. In: Proceedings of the 2001 FUZZ-IEEE, Melbourne, Australia (2001)

    Google Scholar 

  17. Chen, Y., Mazlack, L.J., Lu, L.J.; Maps, learning fuzzy cognitive, from data by Ant colony optimization. In: GECCO12, Philadelphia, Pennsylvania, USA, 7–11 July 2012

    Google Scholar 

  18. Chunmei, L., Yue, H.: Learning, cellular automata of fuzzy cognitive map. In: International Conference on System Science and Engineering, Dalian, China (2012)

    Google Scholar 

  19. Dickerson, J.A., Kosko, B.: Virtual worlds as fuzzy cognitive maps. In: Proceedings of IEEE Virtual Reality Annual International Symposium, pp. 471–477. New York (1993)

    Google Scholar 

  20. Dickerson, J.A., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Presence 3(2), 173–189 (1994)

    Google Scholar 

  21. Ding, Z., Li, D., Jia, J.: First study of fuzzy cognitive map learning using ants colony optimization. J. Comput. Inf. Syst. 7(13), 4756–4763 (2011)

    Google Scholar 

  22. Froelich, W., Papageorgiou, E.I., Samarinas, M., Skriapas, K.: Application of evolutionary FCMs to the long-term prediction of prostate cancer. Appl. Soft Comput. 12(12), 3810–3817 (2012)

    Article  Google Scholar 

  23. Froelich, W., Wakulicz-Deja, A.: Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps: a comparative study. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intelligent Systems for Knowledge Management, SCI 252, pp. 153–174. Springer, Berlin (2009)

    Google Scholar 

  24. Ghaderi, S.F., Azadeh, A.: Pourvalikhan Nokhandan, B., Fathi, E.: Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map. Expert Syst. Appl. 39(5), 4635–4646 (2012)

    Article  Google Scholar 

  25. Glykas, M.: Fuzzy Cognitive Maps-Theories, Methodologies, Tools and Applications. Springer, Berlin (2010)

    Book  Google Scholar 

  26. Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)

    Google Scholar 

  27. Herrera, F., Lozano, M., Verdegay, J.L.: Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artif. Intell. Rev. 12, 265–319 (1998)

    Article  MATH  Google Scholar 

  28. Huerga, A.V.: A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the 16th International Workshop on Qualitative Reasoning (2002)

    Google Scholar 

  29. Kok, K.: The potential of fuzzy cognitive maps for semi-quantitative scenario development, with an example from Brazil. Global Environ. Change 19, 122–133 (2009)

    Article  Google Scholar 

  30. Konar, A., Chakraborty, U.K.: Reasoning and unsupervised learning in a fuzzy cognitive map. Inf. Sci. 170, 419–441 (2005)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  32. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)

    MATH  Google Scholar 

  33. Kottas, T.L., Boutalis, Y.S., Christodoulou, M.A.: Fuzzy cognitive networks: a general framework. Intell. Decis. Technol. 1, 183–196 (2007)

    Google Scholar 

  34. 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: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 364–371 (2001)

    Google Scholar 

  35. Lin, C.: An immune algorithm for complex fuzzy cognitive map partitioning. In: proceeding of Genetic and Evolutionary Computation Conference, GEC Summit 2009, Shanghai, China (2009)

    Google Scholar 

  36. Lopez, C., Salmeron, J.L.: Dynamic risks modelling in ERP maintenance projects with FCM. Information Sciences (2013) in press, available in: http://www.sciencedirect.com/science/article/pii/S0020025512003945

  37. Luo, X., Wei, X., Zhang, J.: Game-based learning model using fuzzy cognitive map. In: 1st ACM International Workshop on Multimedia Technologies for Distance Learning, Co-located with the 2009 ACM International Conference on Multimedia, pp. 67–76 (2009)

    Google Scholar 

  38. Madeiro, S.S., Von Zuben, F.J.: Gradient-based algorithms for the automatic construction of fuzzy cognitive maps. In: 11th International Conference on Machine Learning and Applications (2012)

    Google Scholar 

  39. Mateou, N.H., Andreou, A.S.: A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps. J. Intell. Fuzzy Syst. 19, 151–170 (2008)

    MATH  Google Scholar 

  40. Miao, Y., Liu, Z.Q., Siew, C.K., Miao, C.Y.: Dynamical cognitive network: an extension of fuzzy cognitive map. IEEE Trans. Fuzzy Syst. 9, 760–770 (2001)

    Article  Google Scholar 

  41. Papageorgiou, E.I., Froelich, W.: Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012)

    Article  Google Scholar 

  42. Papageorgiou, E.I., Groumpos, P.P.: A new hybrid learning algorithm for fuzzy cognitive maps learning. Appl. Soft Comput. 5, 409–431 (2005)

    Article  Google Scholar 

  43. Papageorgiou, E.I., Parsopoulos, K.E., Stylios, C.D., Groumpos, P.P., Vrahatis, M.N.: Fuzzy cognitive maps learning using particle swarm optimization. Int. J. Intell. Inf. Syst. 25(1), 95–121 (2005)

    Article  Google Scholar 

  44. Papageorgiou, E.I.: A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 11, 500–513 (2011)

    Article  Google Scholar 

  45. Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps: a review study. IEEE Trans. SMC Part C. 42(2), 150–163 (2012)

    Google Scholar 

  46. Papageorgiou, E.I., Kontogianni, A.: Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application, in book: International Perspectives on Global Environmental Change, Eds: Stephen S. Young and Steven E. Silvern, pp. 427–450 (2012) ISBN 978-953-307-815-1

    Google Scholar 

  47. Papageorgiou, E.I., Froelich, W.: Application of evolutionary fuzzy cognitive maps for prediction of pneumonia state. IEEE Trans. Inf. Technol. Biomed. 16(1), 143–149 (2012)

    Google Scholar 

  48. Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning fuzzy cognitive map causal links. Soft Comput. J. 9, 846–857 (2005)

    Article  MATH  Google Scholar 

  49. Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive maps research during the last decade. IEEE Trans. Fuzzy Syst. 21(1), 66-79 (2013)

    Google Scholar 

  50. Papageorgiou, E.I., Spyridonos, P., Glotsos, D., Stylios, C.D., Groumpos, P.P., Nikiforidis, G.: Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl. Soft Comput. 8, 820–828 (2008)

    Article  Google Scholar 

  51. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Fuzzy cognitive map learning based on nonlinear Hebbian rule. Lecture Notes in Computer Science, vol. 2903, pp. 256–268 (2003)

    Google Scholar 

  52. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Active Hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approx. Reason. 37, 219249 (2004)

    Article  MathSciNet  Google Scholar 

  53. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int. J. Human Comput. Stud. 64, 727–743 (2006)

    Article  Google Scholar 

  54. Papakostas, G.A., Koulouriotis, D.E., Polydoros, A.S., Tourassis, V.D.: Towards Hebbian learning of fuzzy cognitive maps in pattern classification problems. Expert Syst. Appl. 39(12), 10620–10629 (2012)

    Article  Google Scholar 

  55. Papakostas, G.A., Polydoros, A.S., Koulouriotis, D.E., Tourassis, V.D.: Training fuzzy cognitive maps by using Hebbian learning algorithms: a comparative study. IEEE Int. Conf. Fuzzy Syst. (FUZZ) 2011, 851–858 (2011)

    Google Scholar 

  56. Park, K.S., Kim, S.H.: Fuzzy cognitive maps considering time relationships. Int. J. Human Comput. Stud. 42(2), 157–168 (1995)

    Article  Google Scholar 

  57. Pedrycz, W.: The design of cognitive maps: a study in synergy of granular computing and evolutionary optimization. Expert Syst. Appl. 37(10), 7288–7294 (2010)

    Article  Google Scholar 

  58. Peng, Z., Yang, B., Fang, W.: A learning algorithm of fuzzy cognitive map in document classification. In: Proceedings of 5th International Conference on Fuzzy Systems and Knowledge, Discovery, vol. 1, pp. 501–504 (2008)

    Google Scholar 

  59. Petalas, Y.G., Papageorgiou, E.I., Parsopoulos, K.E., Groumpos, P.P., Vrahatis, M.N.: Fuzzy cognitive maps learning using memetic algorithms. In: Proceedings of the International Conference of Computational Methods in Sciences and Engineering (ICCMSE) (2005)

    Google Scholar 

  60. Ren, Z.: Learning fuzzy cognitive maps by a hybrid method using nonlinear Hebbian learning and extended great deluge. In: Proceedings of the 23rd Midwest Artificial Intelligence and Cognitive Science Conference (2012)

    Google Scholar 

  61. Rodriguez-Repiso, L., Setchi, R., Salmeron, J.L.: Modelling IT projects success with fuzzy cognitive maps. Expert Syst. Appl. 32, 543559 (2007)

    Article  Google Scholar 

  62. Ruan, D., Mkrtchyan, L.: Using belief degree-distributed fuzzy cognitive maps for safety culture assessment. Adv. Intell. Soft Comput. 124, 501–510 (2011)

    Article  Google Scholar 

  63. Salmeron, J.L.: Supporting decision makers with fuzzy cognitive maps. Res. Technol. Manage. 52(3), 53–59 (2009)

    MathSciNet  Google Scholar 

  64. Salmeron, J.L.: Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowl. Based Syst. 22(4), 275–278 (2009)

    Article  MathSciNet  Google Scholar 

  65. Salmeron, J.L.: Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Syst. Appl. 37(12), 7581–7588 (2010)

    Article  Google Scholar 

  66. Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. App. Soft Comput. 12(12), 37043710 (2012)

    Google Scholar 

  67. Salmeron, J.L., Lopez, C.: Forecasting risk impact on ERP maintenance with augmented fuzzy cognitive maps. IEEE Trans. Softw. Eng. 38(2), 439–452 (2012)

    Article  Google Scholar 

  68. Salmeron, J.L., Papageorgiou, E.I.: A fuzzy grey cognitive maps-based decision support system for radiotherapy treatment planning. Knowl. Based Syst. 30(1), 151–160 (2012)

    Article  MathSciNet  Google Scholar 

  69. Salmeron, J.L., Vidal, R., Mena, A.: Ranking fuzzy cognitive map based scenarios with TOPSIS. Expert Syst. Appl. 39(3), 2443–2450 (2012)

    Article  Google Scholar 

  70. Schneider, M., Shnaider, E., Kandel, A., Chew, G.: Automatic construction of FCMs. Fuzzy Sets Syst. 93(2), 161–172 (1998)

    Article  Google Scholar 

  71. Slon, G., Yastrebov, A.: Optimization and adaptation of dynamic models of fuzzy relational cognitive maps. Lecture Notes in Artificial Intelligence, LNAI, vol. 6743, pp. 95–102 (2011)

    Google Scholar 

  72. Song, H.J., Miao, C.Y., Wuyts, R., Shen, Z.Q., D’Hondt, M., Catthoor, F.: An extension to fuzzy cognitive maps for classification and prediction. IEEE Trans. Fuzzy Syst. 19(1), 116–135 (2011)

    Article  Google Scholar 

  73. Stach, W.: Learning and aggregation of fuzzy cognitive maps an evolutionary approach. Ph.D. Thesis, University of Alberta. http://gradworks.umi.com/NR/62/NR62921.html (2010)

  74. Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 53, 371–401 (2005)

    Article  MathSciNet  Google Scholar 

  75. Stach, W., Kurgan, L., Pedrycz, W.: Parallel learning of large fuzzy cognitive maps. In: Proceedings of the International Joint Conference on, Neural Networks, pp. 1584–1589 (2007)

    Google Scholar 

  76. Stach, W., Kurgan, L.A., Pedrycz, W.; Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: Proceedings of the World Congress on, Computational Intelligence, pp. 1975–1981 (2008)

    Google Scholar 

  77. Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Learning fuzzy cognitive maps with required precision using genetic algorithm approach. Electron. Lett. 40(24), 1519–1520 (2004)

    Article  Google Scholar 

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

    Article  Google Scholar 

  79. Stylios, C.D., Groumpos, P.P.: Modeling complex systems using fuzzy cognitive maps. IEEE Trans. Syst. Man Cybern. Part A 34, 155–162 (2004)

    Google Scholar 

  80. Taber, R.: Knowledge processing with fuzzy cognitive maps. Expert Syst. Appl. 2, 83–87 (1991)

    Article  Google Scholar 

  81. Taber, R., Yager, R.R., Helgason, C.M.: Quantization effects on the equilibrium behavior of combined fuzzy cognitive maps. Int. J. Intell. Syst. 22, 181–202 (2007)

    Article  MATH  Google Scholar 

  82. Tsadiras, A.K., Kouskouvelis, I., Margaritis, K.G.: Using fuzzy cognitive maps as a decision support system for political decisions. Lecture Notes in Computer Science, vol. 2563, pp. 172–181. Springer, Boston (2003)

    Google Scholar 

  83. Tsadiras, A.K.: Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Inf. Sci. 178(20), 3880–3894 (2008)

    Article  Google Scholar 

  84. Vascak, J.: Approaches in adaptation of fuzzy cognitive maps for navigation purposes. In: Proceedings SAMI 2010–8th International Symposium on Applied Machine Intelligence and Informatics, art. no. 5423716, pp. 31–36 (2010)

    Google Scholar 

  85. Xirogiannis, G., Glykas, M.: Fuzzy cognitive maps in business analysis and performance-driven change. IEEE Trans. Eng. Manage. 51, 334351 (2004)

    Article  Google Scholar 

  86. Yastrebov, A., Piotrowska, K.: Simulation analysis of multistep algorithms of relational cognitive maps learning. In: Yastrebov, A., Kuźmińska-Sołśnia, B., Raczynska, M. (eds.) Computer Technologies in Science, Technology and Education. Institute for Sustainable Technologies–National Research Institute, Radom, pp. 126–137 (2012)

    Google Scholar 

  87. Yesil, E., Urbas, L.: Big bang: big crunch learning method for fuzzy cognitive maps. World Acad. Sci. Eng. Technol. 71, 815–8124 (2010)

    Google Scholar 

  88. Zhu, Y., Zhang, W.: An integrated framework for learning fuzzy cognitive map using RCGA and NHL algorithm. In: International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM (2008)

    Google Scholar 

  89. Zhaowei, R.: Learning fuzzy cognitive maps by a hybrid method using nonlinear Hebbian learning and extended Great Deluge algorithm. In: Association for the Advancement of Artificial Intelligence (www.aaai.org) (2012)

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Papageorgiou, E.I., Salmeron, J.L. (2014). Methods and Algorithms for Fuzzy Cognitive Map-based Modeling. In: Papageorgiou, E. (eds) Fuzzy Cognitive Maps for Applied Sciences and Engineering. Intelligent Systems Reference Library, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39739-4_1

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