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2014 | OriginalPaper | Buchkapitel

1. Methods and Algorithms for Fuzzy Cognitive Map-based Modeling

verfasst von : Elpiniki I. Papageorgiou, Jose L. Salmeron

Erschienen in: Fuzzy Cognitive Maps for Applied Sciences and Engineering

Verlag: Springer Berlin Heidelberg

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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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Nächstes Kapitel Fuzzy Cognitive Maps as Representations of Mental Models and Group Beliefs
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Literatur
1.
Acampora, G., Loia, V.: On the temporal granularity in fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 19(6), 1040–1057 (2011)CrossRef
2.
Aguilar, J.: A dynamic fuzzy cognitive map approach based on random neural networks. Int. J. Comput. Cogn. 1(4), 91–107 (2003)
3.
Aguilar, J., Contreras, J.: The FCM designer tool in fuzzy cognitive maps. Stud. Fuzziness Soft Comput. 247, 71–87 (2010)CrossRef
4.
Alizadeh, S., Ghazanfari, M.: Learning FCM by chaotic simulated annealing. Chaos, Solitons Fractals 41, 1182–1190 (2008)CrossRef
5.
Alizadeh, S., Ghazanfari, M., Fathian, M.: Using data mining for learning and clustering FCM. Int. J. Comput. Intell. 4(2), 118–125 (2008)
6.
Alizadeh, S., Ghazanfari, M., Jafari, M., Hooshmand, S.: Learning FCM by Tabu search. Int. J. Comput. Sci. 2, 143–149 (2008)
7.
Alter, S.L.: Decision Support Systems: Current Practice and Continuing Challenge. Addison Wesley, Reading (1980)
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)CrossRef
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)CrossRef
10.
Axelrod, R.: Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)
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)CrossRef
12.
Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11(1), 1014–1020 (2010)CrossRef
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)CrossRef
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)CrossRef
15.
Carvalho, J.P.: Rule based fuzzy cognitive maps in humanities, social sciences and economics. Stud. Fuzziness Soft Comput. 273, 289–300 (2012)CrossRef
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)
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
18.
Chunmei, L., Yue, H.: Learning, cellular automata of fuzzy cognitive map. In: International Conference on System Science and Engineering, Dalian, China (2012)
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)
20.
Dickerson, J.A., Kosko, B.: Virtual worlds as fuzzy cognitive maps. Presence 3(2), 173–189 (1994)
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)
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)CrossRef
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)
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)CrossRef
25.
Glykas, M.: Fuzzy Cognitive Maps-Theories, Methodologies, Tools and Applications. Springer, Berlin (2010)CrossRef
26.
Hebb, D.O.: The Organization of Behavior. Wiley, New York (1949)
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)CrossRefMATH
28.
Huerga, A.V.: A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of the 16th International Workshop on Qualitative Reasoning (2002)
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)CrossRef
30.
31.
32.
Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, Englewood Cliffs (1992)MATH
33.
Kottas, T.L., Boutalis, Y.S., Christodoulou, M.A.: Fuzzy cognitive networks: a general framework. Intell. Decis. Technol. 1, 183–196 (2007)
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)
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)
36.
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)
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)
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
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)CrossRef
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)CrossRef
42.
Papageorgiou, E.I., Groumpos, P.P.: A new hybrid learning algorithm for fuzzy cognitive maps learning. Appl. Soft Comput. 5, 409–431 (2005)CrossRef
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)CrossRef
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)CrossRef
45.
Papageorgiou, E.I.: Learning algorithms for fuzzy cognitive maps: a review study. IEEE Trans. SMC Part C. 42(2), 150–163 (2012)
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
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)
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)CrossRefMATH
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)
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)CrossRef
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)
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)MathSciNetCrossRef
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)CrossRef
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)CrossRef
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)
56.
Park, K.S., Kim, S.H.: Fuzzy cognitive maps considering time relationships. Int. J. Human Comput. Stud. 42(2), 157–168 (1995)CrossRef
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)CrossRef
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)
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)
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)
61.
Rodriguez-Repiso, L., Setchi, R., Salmeron, J.L.: Modelling IT projects success with fuzzy cognitive maps. Expert Syst. Appl. 32, 543559 (2007)CrossRef
62.
Ruan, D., Mkrtchyan, L.: Using belief degree-distributed fuzzy cognitive maps for safety culture assessment. Adv. Intell. Soft Comput. 124, 501–510 (2011)CrossRef
63.
Salmeron, J.L.: Supporting decision makers with fuzzy cognitive maps. Res. Technol. Manage. 52(3), 53–59 (2009)MathSciNet
64.
Salmeron, J.L.: Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowl. Based Syst. 22(4), 275–278 (2009)MathSciNetCrossRef
65.
Salmeron, J.L.: Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Syst. Appl. 37(12), 7581–7588 (2010)CrossRef
66.
Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. App. Soft Comput. 12(12), 37043710 (2012)
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)CrossRef
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)MathSciNetCrossRef
69.
Salmeron, J.L., Vidal, R., Mena, A.: Ranking fuzzy cognitive map based scenarios with TOPSIS. Expert Syst. Appl. 39(3), 2443–2450 (2012)CrossRef
70.
Schneider, M., Shnaider, E., Kandel, A., Chew, G.: Automatic construction of FCMs. Fuzzy Sets Syst. 93(2), 161–172 (1998)CrossRef
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)
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)CrossRef
73.
74.
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 53, 371–401 (2005)MathSciNetCrossRef
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)
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)
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)CrossRef
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)CrossRef
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)
80.
Taber, R.: Knowledge processing with fuzzy cognitive maps. Expert Syst. Appl. 2, 83–87 (1991)CrossRef
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)CrossRefMATH
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)
83.
Tsadiras, A.K.: Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Inf. Sci. 178(20), 3880–3894 (2008)CrossRef
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)
85.
Xirogiannis, G., Glykas, M.: Fuzzy cognitive maps in business analysis and performance-driven change. IEEE Trans. Eng. Manage. 51, 334351 (2004)CrossRef
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)
87.
Yesil, E., Urbas, L.: Big bang: big crunch learning method for fuzzy cognitive maps. World Acad. Sci. Eng. Technol. 71, 815–8124 (2010)
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)
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)
Metadaten
Titel
Methods and Algorithms for Fuzzy Cognitive Map-based Modeling
verfasst von
Elpiniki I. Papageorgiou
Jose L. Salmeron
Copyright-Jahr
2014
Verlag
Springer Berlin Heidelberg
Buch
Fuzzy Cognitive Maps for Applied Sciences and Engineering

Print ISBN: 978-3-642-39738-7

Electronic ISBN: 978-3-642-39739-4

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-642-39739-4_1