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
Aguilar, J.: Dynamic random fuzzy cognitive maps. Computación y Sistemas 7(4), 260–270 (2004)
Dickerson, J., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Presence 3(2), 173–189 (1994)
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)
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)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, New jersey (1999)
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)
Iakovidis, D., Papageorgiou, E.: Intuitionistic fuzzy cognitive maps for medical decision making. IEEE Trans. Inf Technol. Biomed. 15(1), 100–107 (2011)
Kandasamy, W., Smarandache, F., Ilanthenral, K.: Elementary Fuzzy Matrix and Fuzzy Models for Social Scientists. Automaton, Los angeles (2007)
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)
Khan, M., Quaddus, M.: Group decision support using fuzzy cognitive maps for causual resoning. Group Decis. Negot. 13, 463–480 (2004)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986)
Lazzerini, B., Mkrtchyan, L.: Analyzing risk impact factors using extenden fuzzy cognitive maps. IEEE Syst. J. 5(2), 288–297 (2011)
Papageorgiou, E.: Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Comput. Methods Programs Biomed. 105, 233–245 (2012)
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)
Papageorgiou, E., Froelich, W.: Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps. Neurocomputing 92, 28–35 (2012)
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)
Piotrowska, K.: Intelligent expert system based on cognitive maps. Studia Informatica 33(2A (105)), 605–616 (2012)
Salmeron, J., Papageorgiou, E.: A fuzzy grey cognitive maps-based decision support system for radiotherapy treatment planning. Knowl.-Based Syst. 30, 151–160 (2012)
Schneider, M., Shnaider, E., Kandel, A., Chew, G.: Automatic construction of fcms. Fuzzy Sets Syst. 93, 161–172 (1998)
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)
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)
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)
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)
Stach, W., Kurgan, L., Pedrycz, W.: A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets Syst. 161, 2515–2532 (2010)
Stylios, C., Groumpos, P.: Fuzzy cognitive maps: a model for intelligent supervisory control system. Comput. Ind. 39, 229–238 (1999)
Stylios, C., Groumpos, P., Papageorgiou, E.: Active hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approximate Reasoning 37, 219–249 (2004)
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)
Tsanas, A., Little, M.: Uci machine learning repository (2009). http://archive.ics.uci.edu/ml
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)
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)
Yaman, D., Polat, S.: A fuzzy cognitive map approach for effect-based operations: an illustrative case. Inf. Sci. 179, 382–403 (2009)
Yastrebov, A., Gad, S., Słoń, G.: Cognitive modeling in decision monitoring systems. Stud. Mater. Pol. Assoc. Knowl. Manage. 47, 64–77 (2011)
Yastrebov, A., Grzywaczewski, M.: Design of multistep algorithms and local optimal input for dynamic system identification. Control Cybern. 21(3/4) (1992)
Yastrebov, A., Grzywaczewski, M., Gad, S.: Analysis of a certain class of discrete multidimensional system of extremal control. SAMS 24, 121–133 (1996)
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)
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)
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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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