The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.
A Review of Fuzzy Cognitive Maps Research During the Last Decade
Abstract
This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kosko, B. (1986) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(86)80040-2 · Cited by 3,576
Genetic learning of fuzzy cognitive maps
Stach, W., Kurgan, L., Witold Pedrycz, C., & Reformat, M. (2005) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2005.01.009 · Cited by 531
Modeling Complex Systems Using Fuzzy Cognitive Maps
Stylios, C. D., & Groumpos, P. P. (2004) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2003.818878 · Cited by 476
Virtual Worlds as Fuzzy Cognitive Maps
Dickerson, J. A., & Kosko, B. (1994) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1994.3.2.173 · Cited by 426
Kok, K. (2009) · Global Environmental Change · doi:10.1016/j.gloenvcha.2008.08.003 · Cited by 375
Benchmarking main activation functions in fuzzy cognitive maps
Bueno, S., & Salmeron, J. L. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.06.072 · Cited by 337
Self-citationLearning Algorithms for Fuzzy Cognitive Maps—A Review Study
Papageorgiou, E. I. (2012) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2138694 · Cited by 322
Self-citationReadings in Fuzzy Sets for Intelligent Systems
(1993) · Elsevier eBooks · doi:10.1016/c2013-0-08304-1 · Cited by 320
van Vliet, M., Kok, K., & Veldkamp, T. (2010) · Futures · doi:10.1016/j.futures.2009.08.005 · Cited by 288
Papageorgiou, E. I. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2009.12.010 · Cited by 247
Self-citation
Cited by
Tools and methods in participatory modeling: Selecting the right tool for the job
Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., Prell, C., Zellner, M., Paolisso, M., Jordan, R., et al. (2018) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2018.08.028 · Cited by 428
Gray, S. A., Gray, S., De Kok, J. L., Helfgott, A. E. R., O'Dwyer, B., Jordan, R., & Nyaki, A. (2015) · Ecology and Society · doi:10.5751/es-07396-200211 · Cited by 284
Charitopoulos, A., Rangoussi, M., & Koulouriotis, D. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00200-8 · Cited by 89
Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector
Choi, Y., Lee, H., & Irani, Z. (2018) · Annals of Operations Research · doi:10.1007/s10479-016-2281-6 · Cited by 76
Olazabal, M., Neumann, M. B., Foudi, S., & Chiabai, A. (2018) · Systems Research and Behavioral Science · doi:10.1002/sres.2519 · Cited by 56
Metadata
- Authors
- Papageorgiou, E. I., & Salmeron, J. L.
- Venue
- IEEE Transactions on Fuzzy Systems
- Published
- 2013-02
- Volume
- 21
- Issue
- 1
- Pages
- 66-79
- ISSN
- 1063-6706 (ISSN-L)
- 1941-0034
- OpenAlex
- W2069178824
- Cited by
- 522
- References
- 79
- FWCI
- 23.11
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A Review of Fuzzy Cognitive Maps Research During the Last Decade. https://app.cochat.ai/papers/10.1109/tfuzz.2012.2201727
- The paper
- Papageorgiou, E. I., & Salmeron, J. L. (2013). A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/tfuzz.2012.2201727
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
