{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T13:50:11Z","timestamp":1780494611165,"version":"3.54.1"},"reference-count":219,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"National Science Centre","doi-asserted-by":"publisher","award":["2024\/53\/B\/ST6\/00021"],"award-info":[{"award-number":["2024\/53\/B\/ST6\/00021"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002848","name":"Agencia Nacional de Investigaci\u00f3n y Desarrollo","doi-asserted-by":"publisher","award":["1240293"],"award-info":[{"award-number":["1240293"]}],"id":[{"id":"10.13039\/501100002848","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle static and temporal data. Over the last decade, there has been a noticeable increase in the number of contributions dedicated to developing FCM-based models and algorithms for structured pattern classification and time series forecasting. These models are attractive since they have proven competitive compared to black boxes while providing highly desirable interpretability features. Equally important are the theoretical studies that have significantly advanced our understanding of the convergence behavior and approximation capabilities of FCM-based models. These studies can challenge individuals who are not experts in Mathematics or Computer Science. As a result, we can occasionally find flawed FCM studies that fail to benefit from the theoretical progress experienced by the field. To address all these challenges, this survey paper aims to cover relevant theoretical and algorithmic advances in the field, while providing clear interpretations and practical pointers for both practitioners and researchers. Additionally, we will survey existing tools and software implementations, highlighting their strengths and limitations towards developing FCM-based solutions.<\/jats:p>","DOI":"10.3390\/bdcc10010022","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T15:09:36Z","timestamp":1767712176000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Review on Fuzzy Cognitive Mapping: Recent Advances and Algorithms"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1936-3701","authenticated-orcid":false,"given":"Gonzalo","family":"N\u00e1poles","sequence":"first","affiliation":[{"name":"Department of Intelligent Systems, Tilburg University, 5037 AB Tilburg, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-1859","authenticated-orcid":false,"given":"Agnieszka","family":"Jastrzebska","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Information Science, Warsaw University of Technology, 00-662 Warsaw, Poland"},{"name":"Faculty of Social and Technical Sciences, The John Paul II Catholic University of Lublin, 20-950 Lublin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8035-2887","authenticated-orcid":false,"given":"Isel","family":"Grau","sequence":"additional","affiliation":[{"name":"Information Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"},{"name":"Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1946-0053","authenticated-orcid":false,"given":"Yamisleydi","family":"Salgueiro","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Faculty of Engineering, Universidad de Talca, Campus Curic\u00f3, Curic\u00f3 3340000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8849-4521","authenticated-orcid":false,"given":"Maikel","family":"Leon","sequence":"additional","affiliation":[{"name":"Department of Business Technology, Miami Herbert Business School, University of Miami, Coral Gables, FL 33146, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0020-7373(86)80040-2","article-title":"Fuzzy cognitive maps","volume":"24","author":"Kosko","year":"1986","journal-title":"Int. J. Man-Mach. Stud."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J., and N\u00e1poles, G. (2024). Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature.","DOI":"10.1007\/978-3-031-48963-1"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"K\u00f3czy, L.T. (2023). Fuzzy Cognitive Maps: A Tool for the Modeling and Simulation of Processes and Systems, Springer Nature.","DOI":"10.1007\/978-3-031-37959-8"},{"key":"ref_4","unstructured":"Craiger, P., and Coovert, M.D. (1994, January 26\u201329). Modeling dynamic social and psychological processes with fuzzy cognitive maps. Proceedings of the 1994 IEEE 3rd International Fuzzy Systems Conference, Orlando, FL, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/S0166-3615(98)00139-0","article-title":"Fuzzy Cognitive Maps: A model for intelligent supervisory control systems","volume":"39","author":"Stylios","year":"1999","journal-title":"Comput. Ind."},{"key":"ref_6","unstructured":"Stylios, C.D., Georgopoulos, V.C., and Groumpos, P.P. (1997, January 21\u201323). The use of fuzzy cognitive maps in modeling systems. Proceedings of the 5th IEEE Mediterranean Conference on Control and Systems, Paphos, Cyprus."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.ecolmodel.2017.07.010","article-title":"Using fuzzy cognitive maps for predicting river management responses: A case study of the Esla river basin, Spain","volume":"360","author":"Alonso","year":"2017","journal-title":"Ecol. Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e373","DOI":"10.1002\/bsd2.373","article-title":"Relations between supply chain performance and circular economy implementation: A fuzzy cognitive map-based analysis for sustainable development","volume":"7","author":"Zanon","year":"2024","journal-title":"Bus. Strategy Dev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P., Fattoruso, M., and Norman, M.L. (2019, January 3\u20135). CoFluences: Simulating the Spread of Social Influences via a Hybrid Agent-Based\/Fuzzy Cognitive Maps Architecture. Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, Chicago, IL, USA. SIGSIM-PADS \u201919.","DOI":"10.1145\/3316480.3322887"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102963","DOI":"10.1016\/j.cities.2020.102963","article-title":"A sociotechnical approach to causes of urban blight using fuzzy cognitive mapping and system dynamics","volume":"108","author":"Lousada","year":"2021","journal-title":"Cities"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Karatzinis, G.D., and Boutalis, Y.S. (2025). A review study of fuzzy cognitive maps in engineering: Applications, insights, and future directions. Eng, 6.","DOI":"10.3390\/eng6020037"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, H., Wu, Y., Liu, Y., and Liu, W. (2023, January 26\u201329). A Cross-Layer Framework for LPWAN Management based on Fuzzy Cognitive Maps with Adaptive Glowworm Swarm Optimization. Proceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, UK.","DOI":"10.1109\/WCNC55385.2023.10119004"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9150","DOI":"10.1109\/TEM.2023.3326663","article-title":"Data-driven decision-making to rank products according to online reviews and the interdependencies among product features","volume":"71","author":"Dahooie","year":"2023","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4201","DOI":"10.1109\/TFUZZ.2024.3462631","article-title":"Time Series Forecasting Using Improved Empirical Fourier Decomposition and High-Order Intuitionistic FCM: Applications in Smart Manufacturing Systems","volume":"33","author":"Nikseresht","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"132210","DOI":"10.1109\/ACCESS.2024.3460471","article-title":"A Many-Objective Investigation on Electric Vehicles\u2019 Integration Into Low-Voltage Energy Distribution Networks with Rooftop PVs and Distributed ESSs","volume":"12","author":"Boglou","year":"2024","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"114000","DOI":"10.1016\/j.dss.2023.114000","article-title":"Decision support systems in crowdfunding: A fuzzy cognitive maps (FCM) approach","volume":"173","year":"2023","journal-title":"Decis. Support Syst."},{"key":"ref_17","first-page":"15","article-title":"Comparison of Fuzzy Cognitive Maps and SEM in Estimating the Perception of Corporate Social Responsibility","volume":"69","year":"2024","journal-title":"Neutrosophic Sets Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3998","DOI":"10.1108\/ECAM-08-2023-0828","article-title":"Using fuzzy cognitive maps to explore the dynamic impact on management team resilience in international construction projects","volume":"32","author":"Gao","year":"2024","journal-title":"Eng. Constr. Archit. Manag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Parre\u00f1o, L., and Pablo-Mart\u00ed, F. (2024). Fuzzy cognitive maps for municipal governance improvement. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0294962"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112199","DOI":"10.1016\/j.asoc.2024.112199","article-title":"Assessing Digital Transformation Using Fuzzy Cognitive Mapping Supported by Artificial Intelligence Techniques","volume":"166","author":"Erkan","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s13690-024-01303-7","article-title":"Fuzzy cognitive mapping in participatory research and decision making: A practice review","volume":"82","author":"Sarmiento","year":"2024","journal-title":"Arch. Public Health"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1002\/9781394242252.ch11","article-title":"Implementation of a Neuro-Fuzzy-Based Classifier for the Detection of Types 1 and 2 Diabetes","volume":"11","author":"Kaur","year":"2024","journal-title":"Adv. Fuzzy-Based Internet Med. Things (IoMT)"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sovatzidi, G., Vasilakakis, M., and Iakovidis, D. (2023, January 24\u201326). Automatic Fuzzy Cognitive Maps for Explainable Image-based Pneumonia Detection. Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics, Lamia, Greece.","DOI":"10.1145\/3635059.3635071"},{"key":"ref_24","unstructured":"Abbaspour Onari, M., Grau, I., Nobile, M., and Zhang, Y. (2023). Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107829","DOI":"10.1016\/j.cmpb.2023.107829","article-title":"Case studies of clinical decision-making through prescriptive models based on machine learning","volume":"242","author":"Hoyos","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"101513","DOI":"10.1016\/j.softx.2023.101513","article-title":"In-Cognitive: A web-based Python application for fuzzy cognitive map design, simulation, and uncertainty analysis based on the Monte Carlo method","volume":"23","author":"Koutsellis","year":"2023","journal-title":"SoftwareX"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111940","DOI":"10.1016\/j.asoc.2024.111940","article-title":"Explainability analysis: An in-depth comparison between Fuzzy Cognitive Maps and LAMDA","volume":"164","author":"Benito","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3610771","article-title":"Extensions of Fuzzy Cognitive Maps: A Systematic Review","volume":"56","author":"Schuerkamp","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1007\/s41066-023-00417-7","article-title":"Information flow-based fuzzy cognitive maps with enhanced interpretability","volume":"8","author":"Tyrovolas","year":"2023","journal-title":"Granul. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J., Knox, C.B., Furman, K., Jetter, A., and Gray, S. (2024). Defining and Using Fuzzy Cognitive Mapping. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 1.","DOI":"10.1007\/978-3-031-48963-1_1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1007\/s10729-022-09611-6","article-title":"A clinical decision-support system for dengue based on fuzzy cognitive maps","volume":"25","author":"Hoyos","year":"2022","journal-title":"Health Care Manag. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Knox, C.B., Furman, K., Jetter, A., Gray, S., and Giabbanelli, P.J. (2024). Creating an FCM with Participants in an Interview or Workshop Setting. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 2.","DOI":"10.1007\/978-3-031-48963-1_2"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Giabbanelli, P.J. (2024). Hybrid Simulations. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 4.","DOI":"10.1007\/978-3-031-48963-1"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111078","DOI":"10.1016\/j.knosys.2023.111078","article-title":"On the interpretability of fuzzy cognitive maps","volume":"281","author":"Salgueiro","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schuerkamp, R., and Giabbanelli, P.J. (2024). Analysis of Fuzzy Cognitive Maps. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 5.","DOI":"10.1007\/978-3-031-48963-1"},{"key":"ref_36","first-page":"1","article-title":"Explainability Analysis of the Evaluation Model of the Level of Digital Transformation in MSMEs based on Fuzzy Cognitive Maps: Explainability Analysis on Fuzzy Cognitive Maps","volume":"27","author":"Aguilar","year":"2023","journal-title":"CLEI Electron. J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Schuerkamp, R., and Giabbanelli, P.J. (2024). Extensions of Fuzzy Cognitive Maps. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 6.","DOI":"10.1007\/978-3-031-48963-1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fulara, S.P., Pant, S., Pant, M., and Kumar, S. (2024, January 16\u201318). Hesitant Intuitionistic Fuzzy Cognitive Map Based Fuzzy Time Series Forecasting Method. Proceedings of the International Conference on Intelligent and Fuzzy Systems, Canakkale, Turkey.","DOI":"10.1007\/978-3-031-70018-7_53"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Priya, R., and Martin, N. (2024). Integrated fuzzy soft FCM approach in focused decision-making. Data-Driven Modelling with Fuzzy Sets, CRC Press.","DOI":"10.1201\/9781003487029-1"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TETCI.2023.3327355","article-title":"A Hypersphere Information Granule-Based Fuzzy Classifier Embedded with Fuzzy Cognitive Maps for Classification of Imbalanced Data","volume":"8","author":"Yin","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108851","DOI":"10.1016\/j.fss.2023.108851","article-title":"The synchronization of K-valued Fuzzy cognitive maps","volume":"478","author":"Luo","year":"2024","journal-title":"Fuzzy Sets Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TCYB.2017.2771387","article-title":"Uncertainty propagation in fuzzy grey cognitive maps with Hebbian-like learning algorithms","volume":"49","author":"Salmeron","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Salmeron, J.L., and Ruiz-Celma, A. (2021). Synthetic emotions for empathic building. Mathematics, 9.","DOI":"10.3390\/math9070701"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Garatejo, C., Hoyos, W., and Aguilar, J. (2023, January 16\u201320). An Approach Based on Fuzzy Cognitive Maps with Federated Learning to Predict Severity in Viral Diseases. Proceedings of the 2023 XLIX Latin American Computer Conference (CLEI), La Paz, Bolivia.","DOI":"10.1109\/CLEI60451.2023.10346147"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Pang, R. (2024, January 24\u201326). CE-FCM: Convolution-Enhanced Fuzzy Cognitive Map for Multivariate Time Series Prediction. Proceedings of the 2024 3rd International Symposium on Control Engineering and Robotics, Changsha, China.","DOI":"10.1145\/3679409.3679427"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Orang, O., Erazo-Costa, F.J., Silva, P.C., de Alencar Barreto, G., and Guimar\u00e3es, F.G. (2024, January 23\u201324). A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps. Proceedings of the 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Madrid, Spain.","DOI":"10.1109\/EAIS58494.2024.10570027"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1109\/TFUZZ.2024.3379853","article-title":"Multiple-Input Multiple-Output Randomized Fuzzy Cognitive Map Method for High-Dimensional Time Series Forecasting","volume":"32","author":"Orang","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"127743","DOI":"10.1016\/j.neucom.2024.127743","article-title":"Multivariate time series clustering based on fuzzy cognitive maps and community detection","volume":"590","author":"Teng","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"131429","DOI":"10.1016\/j.energy.2024.131429","article-title":"Enhancing hourly electricity forecasting using fuzzy cognitive maps with sample entropy","volume":"298","author":"Li","year":"2024","journal-title":"Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3992","DOI":"10.1109\/TFUZZ.2024.3386823","article-title":"Causal Discovery from Abundant but Noisy Fuzzy Cognitive Map Set","volume":"32","author":"Teng","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s40537-024-00911-y","article-title":"Blind Federated Learning without initial model","volume":"11","author":"Salmeron","year":"2024","journal-title":"J. Big Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107482","DOI":"10.1016\/j.future.2024.107482","article-title":"Concurrent vertical and horizontal federated learning with fuzzy cognitive maps","volume":"162","author":"Salmeron","year":"2025","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112089","DOI":"10.1016\/j.knosys.2024.112089","article-title":"A revised cognitive mapping methodology for modeling and simulation","volume":"299","author":"Grau","year":"2024","journal-title":"Knowl.-Based Systems"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Apostolopoulos, I.D., Papandrianos, N.I., Papathanasiou, N.D., and Papageorgiou, E.I. (2024). Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study. Bioengineering, 11.","DOI":"10.3390\/bioengineering11020139"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"976","DOI":"10.18421\/TEM132-13","article-title":"The Application of Fuzzy Cognitive Mapping in Education: Trend and Potential","volume":"13","author":"The","year":"2024","journal-title":"TEM J."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7733","DOI":"10.1007\/s10462-022-10319-w","article-title":"Time series forecasting using fuzzy cognitive maps: A survey","volume":"56","author":"Orang","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"300","DOI":"10.51519\/journalisi.v5i1.447","article-title":"A Review of Fuzzy Cognitive Maps Extensions and Learning","volume":"5","author":"Jiya","year":"2023","journal-title":"J. Inf. Syst. Informatics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Schuerkamp, R., Giabbanelli, P.J., Grandi, U., and Doutre, S. (2023, January 10\u201313). How to combine models? Principles and mechanisms to aggregate fuzzy cognitive maps. Proceedings of the 2023 Winter Simulation Conference (WSC), San Antonio, TX, USA.","DOI":"10.1109\/WSC60868.2023.10408326"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1007\/s10462-017-9575-1","article-title":"A review on methods and software for fuzzy cognitive maps","volume":"52","author":"Felix","year":"2017","journal-title":"Artif. Intell. Rev."},{"key":"ref_60","unstructured":"Stylios, C.D., and Groumpos, P.P. (1999, January 28\u201330). Mathematical formulation of fuzzy cognitive maps. Proceedings of the 7th Mediterranean Conference on Control and Automation (MED99), Haifa, Israel."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"105281","DOI":"10.1016\/j.engappai.2022.105281","article-title":"Application of reliability-based back-propagation causality-weighted neural networks to estimate air-overpressure due to mine blasting","volume":"115","author":"Hosseini","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"115455","DOI":"10.1016\/j.eswa.2021.115455","article-title":"Evaluation of the impact of blockchain technology on supply chain using cognitive maps","volume":"184","author":"Budak","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"105660","DOI":"10.1016\/j.asoc.2019.105660","article-title":"Fuzzy cognitive map based quality function deployment approach for dishwasher machine selection","volume":"83","author":"Efe","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"110700","DOI":"10.1016\/j.knosys.2023.110700","article-title":"Deep attention fuzzy cognitive maps for interpretable multivariate time series prediction","volume":"275","author":"Qin","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"108950","DOI":"10.1016\/j.knosys.2022.108950","article-title":"A new fuzzy cognitive maps classifier based on capsule network","volume":"250","author":"Yu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"113763","DOI":"10.1016\/j.eswa.2020.113763","article-title":"An intelligent stock trading decision support system based on rough cognitive reasoning","volume":"160","author":"Li","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"105294","DOI":"10.1016\/j.knosys.2019.105294","article-title":"Evolutionary multitasking fuzzy cognitive map learning","volume":"192","author":"Shen","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.neucom.2022.01.070","article-title":"Modeling implicit bias with fuzzy cognitive maps","volume":"481","author":"Grau","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"107832","DOI":"10.1016\/j.asoc.2021.107832","article-title":"An integrated FCM-FBWM approach to assess and manage the readiness for blockchain incorporation in the supply chain","volume":"112","author":"Irannezhad","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"6083","DOI":"10.1109\/TCYB.2022.3165104","article-title":"Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification","volume":"53","author":"Salgueiro","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s40710-025-00765-3","article-title":"Participatory Modeling and Scenario Analysis for Managing Mediterranean River Basins Using Quasi-nonlinear Fuzzy Cognitive Maps","volume":"12","author":"Papadopoulos","year":"2025","journal-title":"Environ. Process."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"112104","DOI":"10.1016\/j.engappai.2025.112104","article-title":"Enhancing fuzzy cognitive map convergence through supervised and unsupervised learning algorithms: A case study of operational risk assessment in power distribution networks","volume":"161","author":"Baghemoortini","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_73","first-page":"112089","article-title":"On the Convergence of tanh Fuzzy General Grey Cognitive Maps","volume":"299","author":"Gao","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"453","DOI":"10.2478\/amcs-2019-0033","article-title":"On the Convergence of Sigmoidal Fuzzy Grey Cognitive Maps","volume":"29","author":"Harmati","year":"2019","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.1016\/j.ins.2008.05.015","article-title":"Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps","volume":"178","author":"Tsadiras","year":"2008","journal-title":"Inf. Sci."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1016\/j.eswa.2008.06.072","article-title":"Benchmarking main activation functions in fuzzy cognitive maps","volume":"36","author":"Bueno","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s11063-024-11623-y","article-title":"Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification","volume":"56","author":"Augustine","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00146-018-0813-0","article-title":"Re-approaching fuzzy cognitive maps to increase the knowledge of a system","volume":"33","author":"Mpelogianni","year":"2018","journal-title":"AI Soc."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TFUZZ.2009.2017519","article-title":"Adaptive Estimation of Fuzzy Cognitive Maps with Proven Stability and Parameter Convergence","volume":"17","author":"Boutalis","year":"2009","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1016\/j.asoc.2012.01.025","article-title":"Bi-linear adaptive estimation of fuzzy cognitive networks","volume":"12","author":"Kottas","year":"2012","journal-title":"Appl. Soft Comput."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"7283","DOI":"10.1007\/s00521-021-06742-9","article-title":"Global stability of fuzzy cognitive maps","volume":"35","author":"Harmati","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Harmati, I.\u00c1., Hatw\u00e1gner, M.F., and K\u00f3czy, L.T. (2018). On the existence and uniqueness of fixed points of fuzzy cognitive maps. Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Springer.","DOI":"10.1007\/978-3-319-91473-2_42"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.asoc.2013.10.030","article-title":"Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points","volume":"15","author":"Knight","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1587\/transinf.E93.D.2883","article-title":"Design of sigmoid activation functions for fuzzy cognitive maps via Lyapunov stability analysis","volume":"93","author":"Lee","year":"2010","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s11063-016-9534-x","article-title":"Learning and Convergence of Fuzzy Cognitive Maps Used in Pattern Recognition","volume":"45","author":"Papageorgiou","year":"2017","journal-title":"Neural Process. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1109\/TFUZZ.2017.2768327","article-title":"On the accuracy-convergence tradeoff in sigmoid fuzzy cognitive maps","volume":"26","author":"Falcon","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TFUZZ.2020.2973853","article-title":"Unveiling the Dynamic Behavior of Fuzzy Cognitive Maps","volume":"29","author":"Falcon","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"112604","DOI":"10.1016\/j.asoc.2024.112604","article-title":"Estimating the limit state space of quasi-nonlinear Fuzzy Cognitive Maps","volume":"169","author":"Grau","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"129409","DOI":"10.1016\/j.neucom.2025.129409","article-title":"Learning of Fuzzy Cognitive Map models without training data","volume":"623","author":"Grau","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Harmati, I. (2021). Dynamics of fuzzy-rough cognitive networks. Symmetry, 13.","DOI":"10.3390\/sym13050881"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1109\/TCYB.2020.3022527","article-title":"Fuzzy-Rough Cognitive Networks: Theoretical Analysis and Simpler Models","volume":"52","author":"Grau","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"106424","DOI":"10.1016\/j.knosys.2020.106424","article-title":"Algebraic dynamics of k-valued fuzzy cognitive maps and its stabilization","volume":"209","author":"Luo","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"17954","DOI":"10.3934\/math.2025800","article-title":"Theoretical study of interval-valued fuzzy cognitive maps: Inference mechanisms, convergence, and centrality measures","volume":"10","author":"Feng","year":"2025","journal-title":"AIMS Math."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Eleni, V., and Petros, G. (2017, January 3\u20136). New concerns on fuzzy cognitive maps equation and sigmoid function. Proceedings of the 2017 25th Mediterranean Conference on Control and Automation (MED), Valletta, Malta.","DOI":"10.1109\/MED.2017.7984267"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.futures.2009.08.005","article-title":"Linking stakeholders and modellers in scenario studies: The use of Fuzzy Cognitive Maps as a communication and learning tool","volume":"42","author":"Kok","year":"2010","journal-title":"Futures"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Bernard, D., and Giabbanelli, P.J. (2024). Creating FCM Models from Quantitative Data with Evolutionary Algorithms. Fuzzy Cognitive Maps: Best Practices and Modern Methods, Springer Nature. Chapter 7.","DOI":"10.1007\/978-3-031-48963-1_7"},{"key":"ref_97","unstructured":"Koulouriotis, D., Diakoulakis, I., and Emiris, D. (2001, January 27\u201330). Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior. Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001), Seoul, Republic of Korea."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"120729","DOI":"10.1016\/j.eswa.2023.120729","article-title":"PRV-FCM: An extension of fuzzy cognitive maps for prescriptive modeling","volume":"231","author":"Hoyos","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Altundo\u011fan, T.G., and Karak\u00f6se, M. (November, January 12). Genetic Algorithm Based Fuzzy Cognitive Map Concept Relationship Determination and Sigmoid Configuration. Proceedings of the 2020 IEEE International Symposium on Systems Engineering (ISSE), Vienna, Austria.","DOI":"10.1109\/ISSE49799.2020.9272216"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.2298\/FIL1805657H","article-title":"Interval-valued fuzzy cognitive maps with genetic learning for predicting corporate financial distress","volume":"32","author":"Hajek","year":"2018","journal-title":"Filomat"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1177\/15485129231184900","article-title":"Modeling of Russian\u2013Ukrainian war based on fuzzy cognitive map with genetic tuning","volume":"21","author":"Rotshtein","year":"2023","journal-title":"J. Def. Model. Simul."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ramirez-Bautista, J.A., Hern\u00e1ndez-Zavala, A., Huerta-Ruelas, J.A., Hatw\u00e1gner, M.F., Chaparro-C\u00e1rdenas, S.L., and K\u00f3czy, L.T. (2018, January 22\u201327). Detection of Human Footprint Alterations by Fuzzy Cognitive Maps Trained with Genetic Algorithm. Proceedings of the 2018 Seventeenth Mexican International Conference on Artificial Intelligence (MICAI), Guadalajara, Mexico.","DOI":"10.1109\/MICAI46078.2018.00013"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.1007\/s00603-022-02866-z","article-title":"An ANN-Fuzzy Cognitive Map-Based Z-Number Theory to Predict Flyrock Induced by Blasting in Open-Pit Mines","volume":"55","author":"Hosseini","year":"2022","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_104","unstructured":"Bui, T.X. (2023, January 3\u20136). Fast Generation of Heterogeneous Mental Models from Longitudinal Data by Combining Genetic Algorithms and Fuzzy Cognitive Maps. Proceedings of the 56th Hawaii International Conference on System Sciences, HICSS 2023, Maui, HI, USA."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/TFUZZ.2015.2426314","article-title":"Learning of Fuzzy Cognitive Maps with Varying Densities Using A Multiobjective Evolutionary Algorithm","volume":"24","author":"Chi","year":"2016","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_106","unstructured":"Mart\u00edn-Vide, C., Neruda, R., and Vega-Rodr\u00edguez, M.A. (2017, January 18\u201320). Learning Interval-Valued Fuzzy Cognitive Maps with PSO Algorithm for Abnormal Stock Return Prediction. Proceedings of the Theory and Practice of Natural Computing, Prague, Czech Republic."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Mendon\u00e7a, M., Pal\u00e1cios, R.H.C., Papageorgiou, E.I., and de Souza, L.B. (2020, January 19\u201324). Multi-robot exploration using Dynamic Fuzzy Cognitive Maps and Ant Colony Optimization. Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK.","DOI":"10.1109\/FUZZ48607.2020.9177814"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1016\/j.cie.2019.06.032","article-title":"A dynamic multiple attribute decision making model with learning of fuzzy cognitive maps","volume":"135","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ref.2019.04.001","article-title":"An improved Elitist\u2013Jaya algorithm for simultaneous network reconfiguration and DG allocation in power distribution systems","volume":"30","author":"Raut","year":"2019","journal-title":"Renew. Energy Focus"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.32604\/csse.2023.027377","article-title":"Cat Swarm with Fuzzy Cognitive Maps for Automated Soil Classification","volume":"44","author":"Dutta","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"4854895","DOI":"10.1155\/2020\/4854895","article-title":"Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation","volume":"2020","author":"Ahmed","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"111771","DOI":"10.1016\/j.knosys.2024.111771","article-title":"Learning high-order fuzzy cognitive maps via multimodal artificial bee colony algorithm and nearest-better clustering: Applications on multivariate time series prediction","volume":"295","author":"Li","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"4055","DOI":"10.1007\/s00500-019-04173-2","article-title":"Learning fuzzy cognitive maps with convergence using a multi-agent genetic algorithm","volume":"24","author":"Yang","year":"2020","journal-title":"Soft Comput."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.asoc.2018.10.038","article-title":"Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm","volume":"74","author":"Yang","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Liu, J., Chi, Y., Zhu, C., and Jin, Y. (2017). A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps. BMC Bioinform., 18.","DOI":"10.1186\/s12859-017-1657-1"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"107441","DOI":"10.1016\/j.asoc.2021.107441","article-title":"Learning large-scale fuzzy cognitive maps using an evolutionary many-task algorithm","volume":"108","author":"Wang","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Yang, Z., Liu, J., and Wu, K. (2019, January 10\u201313). Learning of Boosting Fuzzy Cognitive Maps Using a Real-coded Genetic Algorithm. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC 2019), Wellington, New Zealand.","DOI":"10.1109\/CEC.2019.8789975"},{"key":"ref_118","unstructured":"Szewczyk, R., Zieli\u0144ski, C., and Kaliczy\u0144ska, M. (2017, January 22\u201324). Learning Fuzzy Cognitive Maps Using Evolutionary Algorithm Based on System Performance Indicators. Proceedings of the Automation 2017, Wuhan, China."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/978-3-319-40132-4_5","article-title":"Forecasting Indoor Temperature Using Fuzzy Cognitive Maps with Structure Optimization Genetic Algorithm","volume":"Volume 655","author":"Yastrebov","year":"2016","journal-title":"Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization WCO 2015"},{"key":"ref_120","unstructured":"Martin-Vide, C., Pond, G., and Vega-Rodriguez, M.A. (2019, January 9\u201311). An Analysis of Evolutionary Algorithms for Multiobjective Optimization of Structure and Learning of Fuzzy Cognitive Maps Based on Multidimensional Medical Data. Proceedings of the Theory and Practice of Natural Computing, Kingston, ON, Canada."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s11047-022-09895-1","article-title":"Multiobjective evolutionary algorithm IDEA and k-means clustering for modeling multidimenional medical data based on fuzzy cognitive maps","volume":"22","author":"Yastrebov","year":"2023","journal-title":"Nat. Comput."},{"key":"ref_122","unstructured":"Mart\u00edn-Vide, C., Neruda, R., and Vega-Rodr\u00edguez, M.A. (2017, January 18\u201320). An Evolutionary Algorithm Based on Graph Theory Metrics for Fuzzy Cognitive Maps Learning. Proceedings of the Theory and Practice of Natural Computing, Prague, Czech Republic."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"109708","DOI":"10.1016\/j.asoc.2022.109708","article-title":"Towards improved multifactorial particle swarm optimization learning of fuzzy cognitive maps: A case study on air quality prediction","volume":"130","author":"Liang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.techfore.2017.05.036","article-title":"Comparing supply chain risks for multiple product categories with cognitive mapping and Analytic Hierarchy Process","volume":"131","author":"Mital","year":"2018","journal-title":"Technol. Forecast. Soc. Chang."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i3\/82209","article-title":"Meta Heuristic based Fuzzy Cognitive Map Approach to Support towards Early Prediction of Cognitive Disorders among Children (MEHECOM)","volume":"9","author":"Mythili","year":"2016","journal-title":"Indian J. Sci. Technol."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"105622","DOI":"10.1016\/j.engappai.2022.105622","article-title":"A survey of recently developed metaheuristics and their comparative analysis","volume":"117","author":"Alorf","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1111\/risa.13959","article-title":"Reliability analysis of man\u2013machine systems using fuzzy cognitive mapping with genetic tuning","volume":"43","author":"Rotshtein","year":"2023","journal-title":"Risk Anal."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"3915","DOI":"10.1007\/s12652-018-1116-5","article-title":"Analysis of inter-concept dependencies in disease diagnostic cognitive maps using recurrent neural network and genetic algorithms in time series clinical data for targeted treatment","volume":"10","author":"Duneja","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1109\/TFUZZ.2015.2459756","article-title":"A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive Maps","volume":"24","author":"Liu","year":"2016","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.asoc.2018.05.009","article-title":"Inferring gene regulatory networks with hybrid of multi-agent genetic algorithm and random forests based on fuzzy cognitive maps","volume":"69","author":"Liu","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1049\/trit.2018.1059","article-title":"Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps","volume":"4","author":"Liu","year":"2019","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TFUZZ.2020.2975482","article-title":"A Preference-Based Evolutionary Biobjective Approach for Learning Large-Scale Fuzzy Cognitive Maps: An Application to Gene Regulatory Network Reconstruction","volume":"28","author":"Shen","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Altundo\u011fan, T.G., and Karak\u00f6se, M. (2018, January 20\u201323). An Approach for Online Weight Update Using Particle Swarm Optimization in Dynamic Fuzzy Cognitive Maps. Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/UBMK.2018.8566363"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neucom.2016.10.068","article-title":"Interactive evolutionary optimization of fuzzy cognitive maps","volume":"232","author":"Mls","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_135","unstructured":"Saeed, K., and Homenda, W. (2016, January 14\u201316). A Study on Fuzzy Cognitive Map Optimization Using Metaheuristics. Proceedings of the Computer Information Systems and Industrial Management, Vilnius, Lithuania."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.biosystems.2019.02.010","article-title":"Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts","volume":"179","author":"Yastrebov","year":"2019","journal-title":"Biosystems"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Pocz\u0119ta, K., Kubu\u015b, \u0141., Yastrebov, A., and Papageorgiou, E.I. (2018). Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data. Proceedings of the Recent Advances in Computational Optimization: Results of the Workshop on Computational Optimization WCO 2016, Springer.","DOI":"10.1007\/978-3-319-59861-1_10"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"110037","DOI":"10.1016\/j.asoc.2023.110037","article-title":"Short-term PV power forecasting based on time series expansion and high-order fuzzy cognitive maps","volume":"135","author":"Xia","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1109\/TFUZZ.2020.2998513","article-title":"Multivariate Time Series Forecasting Based on Elastic Net and High-Order Fuzzy Cognitive Maps: A Case Study on Human Action Prediction Through EEG Signals","volume":"29","author":"Shen","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.neucom.2016.11.060","article-title":"A concept reduction approach for fuzzy cognitive map models in decision making and management","volume":"232","author":"Papageorgiou","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.1109\/TFUZZ.2018.2793904","article-title":"Two-Stage Learning Based Fuzzy Cognitive Maps Reduction Approach","volume":"26","author":"Yesil","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neunet.2013.02.008","article-title":"Learning the pseudoinverse solution to network weights","volume":"45","author":"Tapson","year":"2013","journal-title":"Neural Networks"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"106461","DOI":"10.1016\/j.asoc.2020.106461","article-title":"Pseudoinverse learning of Fuzzy Cognitive Maps for multivariate time series forecasting","volume":"95","author":"Vanhoenshoven","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.neunet.2020.01.019","article-title":"Deterministic learning of hybrid Fuzzy Cognitive Maps and network reduction approaches","volume":"124","author":"Mosquera","year":"2020","journal-title":"Neural Networks"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"111825","DOI":"10.1016\/j.knosys.2024.111825","article-title":"Backpropagation through time learning for recurrence-aware long-term cognitive networks","volume":"295","author":"Grau","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"16959","DOI":"10.1007\/s00521-022-07348-5","article-title":"Long short-term cognitive networks","volume":"34","author":"Grau","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neunet.2019.03.012","article-title":"Short-term cognitive networks, flexible reasoning and nonsynaptic learning","volume":"115","author":"Vanhoenshoven","year":"2019","journal-title":"Neural Networks"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"41611","DOI":"10.1007\/s11042-021-11007-7","article-title":"A review on extreme learning machine","volume":"81","author":"Wang","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"117721","DOI":"10.1016\/j.eswa.2022.117721","article-title":"Online learning of windmill time series using Long Short-term Cognitive Networks","volume":"205","author":"Salgueiro","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhang, Y., Wang, J., Qin, J., Yin, H., Yang, Y., and Huang, H. (2023). Time-series forecasting based on fuzzy cognitive maps and GRU-autoencoder. Soft Comput., 1\u201317.","DOI":"10.1007\/s00500-023-08977-1"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"2534","DOI":"10.1007\/s40815-023-01564-4","article-title":"Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures","volume":"25","author":"Karatzinis","year":"2023","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"3391","DOI":"10.1109\/TFUZZ.2018.2831640","article-title":"Time-Series Forecasting Based on High-Order Fuzzy Cognitive Maps and Wavelet Transform","volume":"26","author":"Yang","year":"2018","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Feng, G., Lu, W., and Yang, J. (2021). The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map. Algorithms, 14.","DOI":"10.3390\/a14030069"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"106105","DOI":"10.1016\/j.knosys.2020.106105","article-title":"A robust time series prediction method based on empirical mode decomposition and high-order fuzzy cognitive maps","volume":"203","author":"Liu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"111601","DOI":"10.1016\/j.asoc.2024.111601","article-title":"A comprehensive framework for designing and learning fuzzy cognitive maps at the granular level","volume":"158","author":"Zhou","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1007\/s10489-023-05112-3","article-title":"Sparse and regression learning of large-scale fuzzy cognitive maps based on adaptive loss function","volume":"54","author":"Zhou","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Faghihi, F., Cai, S., Moustafa, A., and Alashwal, H. (2022, January 27\u201329). A Nonsynaptic Memory Based Neural Network for Hand-Written Digit Classification Using an Explainable Feature Extraction Method. Proceedings of the 2022 the 6th International Conference on Information System and Data Mining, Silicon Valley, CA, USA.","DOI":"10.1145\/3546157.3546168"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1109\/TNNLS.2019.2910555","article-title":"Nonsynaptic Error Backpropagation in Long-Term Cognitive Networks","volume":"31","author":"Vanhoenshoven","year":"2020","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"130864","DOI":"10.1016\/j.neucom.2025.130864","article-title":"Inverse simulation learning of Quasi-Nonlinear Fuzzy Cognitive Maps","volume":"650","author":"Salmeron","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neucom.2020.03.079","article-title":"Supervised learning in spiking neural networks with synaptic delay-weight plasticity","volume":"409","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_161","unstructured":"Papadopoulos, H., Andreou, A.S., Iliadis, L., and Maglogiannis, I. (October, January 30). Training Fuzzy Cognitive Maps Using Gradient-Based Supervised Learning. Proceedings of the Artificial Intelligence Applications and Innovations, Paphos, Cyprus."},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shen, Z., and Miao, C. (2011, January 27\u201330). Train Fuzzy Cognitive Maps by gradient residual algorithm. Proceedings of the 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), Taipei, Taiwan.","DOI":"10.1109\/FUZZY.2011.6007485"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"12683","DOI":"10.1109\/ACCESS.2024.3355194","article-title":"Convex Optimization Based High-Order Fuzzy Cognitive Map Modeling and Its Application in Time Series Predicting","volume":"12","author":"Shan","year":"2024","journal-title":"IEEE Access"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Madeiro, S.S., and Zuben, F.J.V. (2012, January 12\u201315). Gradient-Based Algorithms for the Automatic Construction of Fuzzy Cognitive Maps. Proceedings of the 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.64"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"107271","DOI":"10.1016\/j.asoc.2021.107271","article-title":"Classification and feature transformation with Fuzzy Cognitive Maps","volume":"105","author":"Szwed","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"104444","DOI":"10.1016\/j.engappai.2021.104444","article-title":"Structured sparsity learning for large-scale fuzzy cognitive maps","volume":"105","author":"Ding","year":"2021","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1109\/TFUZZ.2020.3005293","article-title":"Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction","volume":"29","author":"Wang","year":"2021","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"113464","DOI":"10.1016\/j.knosys.2025.113464","article-title":"Learning of Quasi-nonlinear Long-term Cognitive Networks using iterative numerical methods","volume":"317","author":"Salgueiro","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"129611","DOI":"10.1016\/j.neucom.2025.129611","article-title":"Learning-based aggregation of Quasi-nonlinear fuzzy cognitive maps","volume":"626","author":"Grau","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Orang, O., da Silva, F.A.R., Silva, P.C.L., Barros, P.H.S.S., Ramos, H.S., and Guimar\u00e3es, F.G. (2024). Traffic Forecasting Using Federated Randomized High-Order Fuzzy Cognitive Maps. Brazilian Conference on Intelligent Systems, Springer.","DOI":"10.1007\/978-3-031-79032-4_31"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Salmeron, J.L., and Ar\u00e9valo, I. (July, January 29). A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research. Proceedings of the Rough Sets: International Joint Conference, IJCRS 2020, Havana, Cuba.","DOI":"10.1007\/978-3-030-52705-1_35"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"10708","DOI":"10.1109\/ACCESS.2023.3238823","article-title":"Poisoning Attacks in Federated Learning: A Survey","volume":"11","author":"Xia","year":"2023","journal-title":"IEEE Access"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"11365","DOI":"10.1109\/JIOT.2021.3128646","article-title":"Data Poisoning Attacks on Federated Machine Learning","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"127663","DOI":"10.1016\/j.neucom.2024.127663","article-title":"Differential privacy in deep learning: A literature survey","volume":"589","author":"Pan","year":"2024","journal-title":"Neurocomputing"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., and Zhang, L. (2016, January 24\u201328). Deep Learning with Differential Privacy. Proceedings of the ACM CCS, Vienna, Austria.","DOI":"10.1145\/2976749.2978318"},{"key":"ref_176","unstructured":"Baraheem, S., and Yao, Z. (2022). A Survey on Differential Privacy with Machine Learning and Future Outlook. arXiv."},{"key":"ref_177","unstructured":"Gentry, C. (June, January 31). Fully Homomorphic Encryption Using Ideal Lattices. Proceedings of the Forty-First Annual ACM Symposium on Theory of Computing, Bethesda, MD, USA."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s40745-023-00475-3","article-title":"A Survey on Differential Privacy for Medical Data Analysis","volume":"11","author":"Liu","year":"2024","journal-title":"Ann. Data Sci."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ins.2023.02.025","article-title":"Model Poisoning Attack in Differential Privacy-Based Federated Learning","volume":"630","author":"Yang","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"110178","DOI":"10.1016\/j.knosys.2022.110178","article-title":"FL-Defender: Combating Targeted Attacks in Federated Learning","volume":"260","author":"Jebreel","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"5166","DOI":"10.1109\/TFUZZ.2022.3169624","article-title":"The Trend-Fuzzy-Granulation-Based Adaptive Fuzzy Cognitive Map for Long-Term Time Series Forecasting","volume":"30","author":"Wang","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"6447","DOI":"10.1038\/s41598-025-91123-8","article-title":"A hybrid time series forecasting approach integrating fuzzy clustering and machine learning for enhanced power consumption prediction","volume":"15","author":"Alsalem","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Qin, S., Wang, J., Zhang, Y., Yin, H., and Huang, H. (2025, January 10\u201312). A Long-Term Forecasting Method for Time Series based on Multi-Scale Fuzzy Information Granulation using Double-Layer Fuzzy Cognitive Maps. Proceedings of the 2025 5th International Conference on Neural Networks, Information and Communication Engineering (NNICE), Guangzhou, China.","DOI":"10.1109\/NNICE64954.2025.11064313"},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s00500-021-06455-0","article-title":"Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition","volume":"26","author":"Yao","year":"2022","journal-title":"Soft Comput."},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Orang, O., Silva, R., de Lima e Silva, P.C., and Guimar\u00e3es, F.G. (2020, January 19\u201324). Solar Energy Forecasting with Fuzzy Time Series Using High-Order Fuzzy Cognitive Maps. Proceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK.","DOI":"10.1109\/FUZZ48607.2020.9177767"},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neucom.2020.03.013","article-title":"Intuitionistic fuzzy grey cognitive maps for forecasting interval-valued time series","volume":"400","author":"Hajek","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"6835","DOI":"10.1007\/s00500-019-04321-8","article-title":"Time series prediction based on intuitionistic fuzzy cognitive map","volume":"24","author":"Luo","year":"2020","journal-title":"Soft Comput."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"109586","DOI":"10.1016\/j.asoc.2022.109586","article-title":"Wind power forecasting based on variational mode decomposition and high-order fuzzy cognitive maps","volume":"129","author":"Qiao","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"4623","DOI":"10.1007\/s00521-023-09290-6","article-title":"Interval-valued prediction of time series based on fuzzy cognitive maps and granular computing","volume":"36","author":"Yu","year":"2024","journal-title":"Neural Comput. Appl."},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Chen, J., Guan, A., and Cheng, S. (2024). Double Decomposition and Fuzzy Cognitive Graph-Based Prediction of Non-Stationary Time Series. Sensors, 24.","DOI":"10.3390\/s24227272"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"103978","DOI":"10.1016\/j.engappai.2020.103978","article-title":"Robust empirical wavelet fuzzy cognitive map for time series forecasting","volume":"96","author":"Gao","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"106359","DOI":"10.1016\/j.knosys.2020.106359","article-title":"Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps","volume":"206","author":"Yuan","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.1109\/TFUZZ.2019.2956904","article-title":"Time Series Prediction Using Sparse Autoencoder and High-Order Fuzzy Cognitive Maps","volume":"28","author":"Wu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"109990","DOI":"10.1016\/j.asoc.2023.109990","article-title":"Using empirical wavelet transform and high-order fuzzy cognitive maps for time series forecasting","volume":"135","author":"Mohammadi","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"127926","DOI":"10.1016\/j.energy.2023.127926","article-title":"A novel wind speed forecasting combined model using variational mode decomposition, sparse auto-encoder and optimized fuzzy cognitive mapping network","volume":"278","author":"Hu","year":"2023","journal-title":"Energy"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TFUZZ.2024.3395833","article-title":"Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting","volume":"32","author":"Ouyang","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s11063-024-11666-1","article-title":"Time Series Prediction Based on LSTM and High-Order Fuzzy Cognitive Map with Attention Mechanism","volume":"56","author":"Teng","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1142\/S0218001408006910","article-title":"Fuzzy cognitive maps for pattern recognition applications","volume":"22","author":"Papakostas","year":"2008","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"10620","DOI":"10.1016\/j.eswa.2012.02.148","article-title":"Towards Hebbian learning of Fuzzy Cognitive Maps in pattern classification problems","volume":"39","author":"Papakostas","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/978-3-319-64286-4_5","article-title":"Fuzzy cognitive maps based models for pattern classification: Advances and challenges","volume":"Volume 360","author":"Espinosa","year":"2018","journal-title":"Studies in Fuzziness and Soft Computing"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"3959","DOI":"10.1109\/TFUZZ.2025.3600567","article-title":"Classic Fuzzy Cognitive Maps Are Not Universal Approximators","volume":"33","author":"Salgueiro","year":"2025","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_202","unstructured":"Zhang, Y., and Liu, H. (2010, January 13\u201315). Classification systems based on fuzzy cognitive maps. Proceedings of the Proceedings\u20144th International Conference on Genetic and Evolutionary Computing, ICGEC 2010, Shenzhen, China."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neucom.2016.10.071","article-title":"Wavelet fuzzy cognitive maps","volume":"232","author":"Wu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neucom.2016.11.059","article-title":"Towards improving the efficiency of the fuzzy cognitive map classifier","volume":"232","author":"Froelich","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neunet.2021.03.001","article-title":"Long-term Cognitive Network-based architecture for multi-label classification","volume":"140","author":"Bello","year":"2021","journal-title":"Neural Networks"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"107415","DOI":"10.1016\/j.asoc.2021.107415","article-title":"Fuzzy cognitive networks with functional weights for time series and pattern recognition applications","volume":"106","author":"Karatzinis","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TFUZZ.2010.2087383","article-title":"An extension to fuzzy cognitive maps for classification and prediction","volume":"19","author":"Song","year":"2011","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"N\u00e1poles, G., Falcon, R., Papageorgiou, E., Bello, R., and Vanhoof, K. (2016, January 24\u201329). Partitive granular cognitive maps to graded multilabel classification. Proceedings of the 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, Canada.","DOI":"10.1109\/FUZZ-IEEE.2016.7737848"},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2017.08.007","article-title":"Fuzzy-Rough Cognitive Networks","volume":"97","author":"Mosquera","year":"2018","journal-title":"Neural Networks"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Abielmona, R., Falcon, R., Zincir-Heywood, N., and Abbass, H.A. (2016). A Granular Intrusion Detection System Using Rough Cognitive Networks. Recent Advances in Computational Intelligence in Defense and Security, Springer.","DOI":"10.1007\/978-3-319-26450-9"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Zheng, H. (2019, January 14\u201319). Bipolar Fuzzy Rough Cognitive Network. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852232"},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"109796","DOI":"10.1016\/j.asoc.2022.109796","article-title":"Neighborhood rough cognitive networks","volume":"131","author":"Li","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_213","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1016\/j.ins.2022.12.012","article-title":"Prolog-based agnostic explanation module for structured pattern classification","volume":"622","author":"Hoitsma","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Wong, M.F., and Tan, C.W. (2024). Aligning crowd-sourced human feedback for reinforcement learning on code generation by large language models. IEEE Trans. Big Data, Early Access.","DOI":"10.1109\/TBDATA.2024.3524104"},{"key":"ref_215","unstructured":"Grau, I., Hernandez, L.D., Sierens, A., Michel, S., Sergeyssels, N., Middag, C., Froyen, V., and Now\u00e9, A. (2021, January 10\u201312). Talking to your Data: Interactive and interpretable data mining through a conversational agent. Proceedings of the 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian-Dutch Conference on Machine Learning BNAIC\/BeneLearn 2021, Luxembourg."},{"key":"ref_216","doi-asserted-by":"crossref","unstructured":"Gray, S.A., Gray, S., Cox, L.J., and Henly-Shepard, S. (2013, January 7\u201310). Mental Modeler: A Fuzzy-Logic Cognitive Mapping Modeling Tool for Adaptive Environmental Management. Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA.","DOI":"10.1109\/HICSS.2013.399"},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"1860010","DOI":"10.1142\/S0218213018600102","article-title":"FCM Expert: Software Tool for Scenario Analysis and Pattern Classification Based on Fuzzy Cognitive Maps","volume":"27","author":"Espinosa","year":"2018","journal-title":"Int. J. Artif. Intell. Tools"},{"key":"ref_218","doi-asserted-by":"crossref","unstructured":"N\u00e1poles, G., Grau, I., Bello, R., Le\u00f3n, M., Vahoof, K., and Papageorgiou, E. (2015, January 2\u20135). A computational tool for simulation and learning of Fuzzy Cognitive Maps. Proceedings of the 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Istanbul, Turkey.","DOI":"10.1109\/FUZZ-IEEE.2015.7337859"},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"e1078","DOI":"10.7717\/peerj-cs.1078","article-title":"FCMpy: A python module for constructing and analyzing fuzzy cognitive maps","volume":"8","author":"Mkhitaryan","year":"2022","journal-title":"PeerJ Comput. Sci."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:24:46Z","timestamp":1767849886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/10\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,6]]},"references-count":219,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["bdcc10010022"],"URL":"https:\/\/doi.org\/10.3390\/bdcc10010022","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,6]]}}}