Abstract
This chapter gives a short introduction to Fuzzy Cognitive Maps (FCMs). It starts with the origin and first applications of Cognitive Maps, then describes the theoretical background of FCMs. Based on the cognitive model, simulations can be performed in order to predict the dynamic behavior of the system and support decision making tasks. The widely applied variations of implementation details are also covered, including their effect on model properties and behavior. A simple example is given to help understanding the theoretical parts, and a short outlook is provided to the possible ways of model creation, too.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Robert A (1976) Structure of decision: the cognitive maps of political elites. Princeton University Press, Princeton, NJ
Yiannis B, Christodoulou MA, Theodoridis D, Kottas T (2014) System identification and adaptive control. Theory and applications of the neurofuzzy and fuzzy cognitive network models
Bueno S, Salmeron JL (2009) Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst Appl 36(3):5221–5229
Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. Presence: Teleoperators Virtual Environ 3(2):173–189
Groumpos PP (2010) Fuzzy cognitive maps: basic theories and their application to complex systems. In: Fuzzy cognitive maps, pp 1–22. Springer
Harmati IÁ, Hatwágner MF, Kóczy LT (2018) On the existence and uniqueness of fixed points of fuzzy cognitive maps. In: International conference on information processing and management of uncertainty in knowledge-based systems, pp 490–500. Springer
Harmati IÁ, Kóczy LT (2018) On the convergence of fuzzy grey cognitive maps. In: Conference on information technology, systems research and computational physics, pp 74–84. Springer
Harmati IÁ, Kóczy LT (2018) On the existence and uniqueness of fixed points of fuzzy set valued sigmoid fuzzy cognitive maps. In: 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–7. IEEE
Hatwagner MF, Koczy LT (2015) Parameterization and concept optimization of FCM models. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE), Istanbul, Aug 2015. IEEE, pp 1–8
Hatwagner MF, Vastag G, Niskanen VA, Kóczy LT (2018) Improved behavioral analysis of fuzzy cognitive map models. In: International conference on artificial intelligence and soft computing pp 630–641. Springer
Hatwágner MF, Vastag G, Niskanen VA, Kóczy LT (2019) Banking applications of FCM models. In: Trends in mathematics and computational intelligence. Springer, pp 61–72
Hatwágner MF, Yesil E, Dodurka MF, Papageorgiou, E., Urbas L, Kóczy LT (2018) Two-stage learning based fuzzy cognitive maps reduction approach. IEEE Trans Fuzzy Syst 26(5):2938–2952
Hatwágner MF, Torma A, Kóczy LT (2015) Parameter dependence of fuzzy cognitive maps’ behaviour. In: 2015 10th Asian control conference (ASCC), May 2015, pp 1–6
Homenda W, Jastrzebska A (2017) Clustering techniques for fuzzy cognitive map design for time series modeling. Neurocomputing 232:3–15
Homenda W, Jastrzebska A, Pedrycz W (2014) Modeling time series with fuzzy cognitive maps. In: 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE). IEEE, pp 2055–2062
Khan MS, Quaddus M (2004) Group decision support using fuzzy cognitive maps for causal reasoning. Group Decis Negot 13(5):463–480
Kosko B (1986) Fuzzy cognitive maps. Int J Man-Mach Stud 24:65–75
Kosko B (1988) Hidden patterns in combined and adaptive knowledge networks. Int J Approx Reason 2(4):377–393
Lu W, Feng G, Liu X, Pedrycz W, Zhang L, Yang J (2019) Fast and effective learning for fuzzy cognitive maps: a method based on solving constrained convex optimization problems. IEEE Trans Fuzzy Syst
Wei L, Yang J, Liu X, Pedrycz W (2014) The modeling and prediction of time series based on synergy of high-order fuzzy cognitive map and fuzzy c-means clustering. Knowl-Based Syst 70:242–255
Martchenko AS, Ermolov IL, Groumpos PP, Poduraev JV, Stylios CD (2003) Investigating stability analysis issues for fuzzy cognitive maps. In: 11th mediterranean conference on control and automation-MED’03
Nawa NE, Furuhashi T (1998) Bacterial evolutionary algorithm for fuzzy system design. In: SMC’98 conference proceedings. 1998 IEEE international conference on systems, man, and cybernetics (Cat No 98CH36218), vol 3. IEEE, pp 2424–2429
Papageorgiou EI (2011) A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl Soft Comput 11:500–513
Papageorgiou EI (2012) Learning algorithms for fuzzy cognitive maps—a review study. IEEE Trans Syst Man Cybern Part C (Applications and Reviews) 42(2):150–163
Papageorgiou EI (2013) Fuzzy cognitive maps for applied sciences and engineering: from fundamentals to extensions and learning algorithms, vol 54. Springer Science & Business Media
Stach W, Kurgan L, Pedrycz W (2010) Expert-based and computational methods for developing fuzzy cognitive maps. In: Glykas M (ed), Fuzzy cognitive maps—advances in theory, methodologies, tools and applications. Springer, pp 23–42
Stylios CD, Groumpos PP et al (1999) Mathematical formulation of fuzzy cognitive maps. In: Proceedings of the 7th mediterranean conference on control and automation, pp. 2251–2261
Tsadiras AK (2008) Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Inf Sci 178(20):3880–3894 (2008)
Yesil E, Urbas L (2010) Big bang-big crunch learning method for fuzzy cognitive maps. World Acad Sci Eng Technol 71:816–825
Yesil E, Urbas L, Demirsoy A (2014) FCM-GUI: a graphical user interface for big bang-big crunch learning of FCM. In: Fuzzy cognitive maps for applied sciences and engineering. Springer, pp 177–198
Zhang W-R (1998) Bipolar fuzzy sets. In: Fuzzy systems proceedings, 1998. The 1998 IEEE international conference on IEEE world congress on computational intelligence, vol 1. IEEE, pp 835–840
Acknowledgements
The research presented in this paper was carried out as part of the EFOP-3.6.2-16-2017-00016 project in the framework of the New Széchenyi Plan. The completion of this project is funded by the European Union and co-financed by the European Social Fund.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Hatwagner, M.F. (2024). Introduction to Fuzzy Cognitive Maps. In: Fuzzy Cognitive Maps. Studies in Fuzziness and Soft Computing, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-031-37959-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-37959-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37958-1
Online ISBN: 978-3-031-37959-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)