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Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps

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Fuzzy Cognitive Maps for Applied Sciences and Engineering

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 54))

Abstract

This chapter is devoted to the synthesis and some analysis of multi-step learning algorithms for fuzzy cognitive maps (FCM). Multi-step supervised learning based on gradient method and unsupervised learning type of differential Hebbian learning (DHL) algorithm were described. Comparative analysis of these methods to one-step algorithms, from the point of view of the influence on the work of the medical prediction system (average percentage prediction error) was performed. FCM learning and testing was based on historical data. Simulation research together with the analysis results were done on prepared software tool ISEMK (Intelligent Expert System based on Cognitive Maps). Selected results of this analysis were presented.

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References

  1. Aguilar, J.: Dynamic random fuzzy cognitive maps. Computación y Sistemas 7(4), 260–270 (2004)

    Google Scholar 

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

    Google Scholar 

  3. Froelich, W., Juszczuk, P.: Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps - a comparative study. In: Nguyen N., Szczerbicki E. (eds.) Intel. Sys. for Know. Management, SCI, vol. 252, pp. 153–174. Springer-Verlag, Heidelberg (2009)

    Google Scholar 

  4. Froelich, W., Wakulicz-Deja, A.: Learning fuzzy cognitive maps from the web for stock market decision support system. In: Wegrzyn-Wolska K., Szczepaniak P. (eds.) Adv. in Intel. Web, ASC, vol. 43, pp. 106–111. Springer-Verlag, Heidelberg (2007)

    Google Scholar 

  5. Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, New jersey (1999)

    Google Scholar 

  6. Hengjie, S., Chunyan, M., Roel, W., Zhigi, S., Catthoor, F.: Implementation of fuzzy cognitive maps based on fuzzy neural network and application in numerical prediction of time series. IEEE Trans. Fuzzy Syst. 18, 233–250 (2010)

    Google Scholar 

  7. Iakovidis, D., Papageorgiou, E.: Intuitionistic fuzzy cognitive maps for medical decision making. IEEE Trans. Inf Technol. Biomed. 15(1), 100–107 (2011)

    Article  Google Scholar 

  8. Kandasamy, W., Smarandache, F., Ilanthenral, K.: Elementary Fuzzy Matrix and Fuzzy Models for Social Scientists. Automaton, Los angeles (2007)

    Google Scholar 

  9. Kannappan, A., Tamilarasi, A., Papageorgiou, E.: 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. Khan, M., Quaddus, M.: Group decision support using fuzzy cognitive maps for causual resoning. Group Decis. Negot. 13, 463–480 (2004)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  12. Lazzerini, B., Mkrtchyan, L.: Analyzing risk impact factors using extenden fuzzy cognitive maps. IEEE Syst. J. 5(2), 288–297 (2011)

    Article  Google Scholar 

  13. Papageorgiou, E.: Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Comput. Methods Programs Biomed. 105, 233–245 (2012)

    Article  Google Scholar 

  14. Papageorgiou, E.: Learning algorithms for fuzzy cognitive maps—a review study. IEEE Trans. Syst. Man Cybern. B Cybern. Part C Appl. Rev. 42(2), 150–163 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Piotrowska, K.: Intelligent expert system based on cognitive maps. Studia Informatica 33(2A (105)), 605–616 (2012)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Siraj, A., Bridges, S., Vaughn, R.: Fuzzy cognitive maps for decision support in an intelligent intrusion detection system. In: IFSA World Congress and 20th NAFIPS International Conference, vol. 4, pp. 2165–2170 (2010)

    Google Scholar 

  21. Słoń, G., Yastrebov, A.: Optimization and adaptation of dynamic models of fuzzy relational cognitive maps. In: Kuznetsov S.E.A. (eds.) RSFDGrC 2011, Lecture Notes in Artificial Intelligence, vol. 6743, pp. 95–102. Springer-Verlag, Heidelberg (2011)

    Google Scholar 

  22. Song, H., Miao, C., Shen, Z., Roel, W., Maja, D., Francky C.: Design of fuzzy cognitive maps using neural networks for predicting chaotic time series. Neural Networks 23, 1264–1275 (2010)

    Google Scholar 

  23. Stach, W., Kurgan, L., Pedrycz, W.: Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 16, 61–72 (2008)

    Article  Google Scholar 

  24. 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 

  25. Stylios, C., Groumpos, P.: Fuzzy cognitive maps: a model for intelligent supervisory control system. Comput. Ind. 39, 229–238 (1999)

    Article  Google Scholar 

  26. Stylios, C., Groumpos, P., Papageorgiou, E.: Active hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approximate Reasoning 37, 219–249 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  27. Stylios, C., Papageorgiou, E.: Fuzzy cognitive maps. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 755–774. Publication Atrium, John Wiley & Son Ltd, New York (2008)

    Google Scholar 

  28. Tsanas, A., Little, M.: Uci machine learning repository (2009). http://archive.ics.uci.edu/ml

  29. Tsanas, A., Little, M., McSharry, P., Raming, L.: Accurate telemonitoring of parkinson’s disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 57(4), 884–893 (2010)

    Article  Google Scholar 

  30. Xiao, Z., Chen, W., Li, L.: An integrated fcm and fuzzy soft set for supplier selection problem based on risk evaluation. Appl. Math. Model. 36, 1444–1454 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  31. Yaman, D., Polat, S.: A fuzzy cognitive map approach for effect-based operations: an illustrative case. Inf. Sci. 179, 382–403 (2009)

    Article  Google Scholar 

  32. Yastrebov, A., Gad, S., Słoń, G.: Cognitive modeling in decision monitoring systems. Stud. Mater. Pol. Assoc. Knowl. Manage. 47, 64–77 (2011)

    Google Scholar 

  33. Yastrebov, A., Grzywaczewski, M.: Design of multistep algorithms and local optimal input for dynamic system identification. Control Cybern. 21(3/4) (1992)

    Google Scholar 

  34. Yastrebov, A., Grzywaczewski, M., Gad, S.: Analysis of a certain class of discrete multidimensional system of extremal control. SAMS 24, 121–133 (1996)

    MATH  Google Scholar 

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

    Google Scholar 

  36. Yastrebov, A., Słoń, G.: Optimization of models of fuzzy relational cognitive maps. In: Yastrebov A., Raczyńska M. (eds.) Computers in Scientific and Educational Activity, pp. 60–71. Institute for Sustainable Technologies—National Research Institute (2011)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science Applications, Kielce University of Technology, Kielce, Poland

    Alexander Yastrebov & Katarzyna Piotrowska

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  1. Alexander Yastrebov
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  2. Katarzyna Piotrowska
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Correspondence to Katarzyna Piotrowska .

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Editors and Affiliations

  1. Department of Computer Engineering, Technological Educational Institute of Central Greece, Lamia, Greece

    Elpiniki I. Papageorgiou

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Yastrebov, A., Piotrowska, K. (2014). Synthesis and Analysis of Multi-Step Learning Algorithms for Fuzzy Cognitive Maps. 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_8

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  • DOI: https://doi.org/10.1007/978-3-642-39739-4_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39738-7

  • Online ISBN: 978-3-642-39739-4

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