Recurrent method for constructing fuzzy cognitive maps for food security assessment

Aleksey Rogachev; Elena Melikhova

doi:10.1051/e3sconf/202021709005

All issues

Volume 217 (2020)

E3S Web Conf., 217 (2020) 09005

References

Open Access

Issue		E3S Web Conf. Volume 217, 2020 International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)


Article Number		09005
Number of page(s)		6
Section		Agricultural and Food Economics
DOI		https://doi.org/10.1051/e3sconf/202021709005
Published online		14 December 2020

E. Antamoshkina, A. Rogachev, Methodical approach to food security assessment and forecasting, Advances in economics, business and management research. Proceedings of the Volgograd State University International Scientific Conference "Competitive, Sustainable and Safe Development of the Regional Economy" (CSSDRE 2019) 184-187 (2019) [Google Scholar]
H.P. Weikard, Phosphorus recycling and food security in the long run: a conceptual modelling approach, Food Security 8, 2, 405-414 (2016) [Google Scholar]
G. Korshunov, V. Lipatnikov, A. Shevchenko, Decision support systems for information protection in the management of the information network. Fuzzy Technologies in the Industry-FTI 2018. Proceedings of the II International Scientific and Practical Conference 418-426 (2018) [Google Scholar]
A. Rogachev, E. Antamoshkina, Mathematical modeling of the food-security level using a fuzzy cognitive approach IOP Conference Series: Earth and Environmental Science 403 (2019) Doi:10.1088/1755-1315/403/1/012181 [Google Scholar]
M. Skvortsova, V. Terekhov, Study of the possibility of applying intellectual methods in creating risk analysis decision support system in the socio-economic sphere, Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 1892-1896 (2019) [CrossRef] [Google Scholar]
A. Rogachev, E. Antamoshkina, E. Melikhova, T. Pleschenko, Fuzzy cognitive modeling of the integral level of regional food security, Lecture Notes in Networks and Systems 129 LNNS 1173-1181 (2020) [Google Scholar]
K. Chrysafiadi, M. Virvou, A Knowledge Representation Approach Using Fuzzy Cognitive Maps for Better Navigation Support in an Adaptive Learning System, Springer Plus 2: 81, 1-13 (2013) [Google Scholar]
R. Isaev, A. Podvesovskii, Generalized Model of Pulse Process for Dynamic Analysis of Sylov's Fuzzy Cognitive Maps CEUR Workshop Proceedings of the Mathematical Modeling Session, International Conference Information Technology and Nanotechnology (MM-ITNT 2017) 1904 7-63 (2017) [Google Scholar]
L. Ginis, G. Gorelova, A. Kolodenkova, Cognitive and simulation modeling of regional economic system development, International Journal of Economics and Financial Issues 6, S5, 97-103 (2016) [Google Scholar]
CR.J.K. Knight, D.J.B. Lloyd, A.S. Penn, Linear and Sigmoidal Fuzzy Cognitive Maps: An Analysis of Fixed Points URL: https://pdfs.semantiescholar.org/(01.07.2018) [Google Scholar]
FCMappers. http://www.fcmappers.net/joomla/ (05. 07. 2018) [Google Scholar]
Pajek: analysis and visualization of large networks. URL: http://pajek.imfm.si/ [Google Scholar]
A. Rogachev, E. Melikhova, E. N. Antamoshkina, Assessment and forecasting of agricultural production and food security based on fuzzy cognitive mathematical models, Proc. of the Lower Volga Agro-University Comp. 397-406 (2019 DOI:10.32786/2071-9485-2019-04-49 [Google Scholar]
O. Osoba, B. Kosko, Fuzzy Cognitive Maps of Public Support for Insurgency and Terrorism, Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 14 (1), 17-32 (2017) [Google Scholar]
E. Gavriliuk, S. Mantserov, Fuzzy reliability model of systems for decision support in technical diagnostics. Fuzzy Technologies in the Industry-FTI 2018. Proceedings of the II International Scientific and Practical Conference 222-234 (2018) [Google Scholar]
M.Shcherbakov. V. Kamaev, A. Brebels et al. Outlier detection and classification in sensor data streams for proactive decision support systems, Journal of Physics: Conference Series 803, 1. 012143 (2017) [Google Scholar]

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[1] E. Antamoshkina, A. Rogachev, Methodical approach to food security assessment and forecasting, Advances in economics, business and management research. Proceedings of the Volgograd State University International Scientific Conference "Competitive, Sustainable and Safe Development of the Regional Economy" (CSSDRE 2019) 184-187 (2019) [Google Scholar]

[2] H.P. Weikard, Phosphorus recycling and food security in the long run: a conceptual modelling approach, Food Security 8, 2, 405-414 (2016) [Google Scholar]

[3] G. Korshunov, V. Lipatnikov, A. Shevchenko, Decision support systems for information protection in the management of the information network. Fuzzy Technologies in the Industry-FTI 2018. Proceedings of the II International Scientific and Practical Conference 418-426 (2018) [Google Scholar]

[4] A. Rogachev, E. Antamoshkina, Mathematical modeling of the food-security level using a fuzzy cognitive approach IOP Conference Series: Earth and Environmental Science 403 (2019) Doi:10.1088/1755-1315/403/1/012181 [Google Scholar]

[5] M. Skvortsova, V. Terekhov, Study of the possibility of applying intellectual methods in creating risk analysis decision support system in the socio-economic sphere, Proceedings of the 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 1892-1896 (2019) [CrossRef] [Google Scholar]

[6] A. Rogachev, E. Antamoshkina, E. Melikhova, T. Pleschenko, Fuzzy cognitive modeling of the integral level of regional food security, Lecture Notes in Networks and Systems 129 LNNS 1173-1181 (2020) [Google Scholar]

[7] K. Chrysafiadi, M. Virvou, A Knowledge Representation Approach Using Fuzzy Cognitive Maps for Better Navigation Support in an Adaptive Learning System, Springer Plus 2: 81, 1-13 (2013) [Google Scholar]

[8] R. Isaev, A. Podvesovskii, Generalized Model of Pulse Process for Dynamic Analysis of Sylov's Fuzzy Cognitive Maps CEUR Workshop Proceedings of the Mathematical Modeling Session, International Conference Information Technology and Nanotechnology (MM-ITNT 2017) 1904 7-63 (2017) [Google Scholar]

[9] L. Ginis, G. Gorelova, A. Kolodenkova, Cognitive and simulation modeling of regional economic system development, International Journal of Economics and Financial Issues 6, S5, 97-103 (2016) [Google Scholar]

[10] CR.J.K. Knight, D.J.B. Lloyd, A.S. Penn, Linear and Sigmoidal Fuzzy Cognitive Maps: An Analysis of Fixed Points URL: https://pdfs.semantiescholar.org/(01.07.2018) [Google Scholar]

[11] FCMappers. http://www.fcmappers.net/joomla/ (05. 07. 2018) [Google Scholar]

[12] Pajek: analysis and visualization of large networks. URL: http://pajek.imfm.si/ [Google Scholar]

[13] A. Rogachev, E. Melikhova, E. N. Antamoshkina, Assessment and forecasting of agricultural production and food security based on fuzzy cognitive mathematical models, Proc. of the Lower Volga Agro-University Comp. 397-406 (2019 DOI:10.32786/2071-9485-2019-04-49 [Google Scholar]

[14] O. Osoba, B. Kosko, Fuzzy Cognitive Maps of Public Support for Insurgency and Terrorism, Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 14 (1), 17-32 (2017) [Google Scholar]

[15] E. Gavriliuk, S. Mantserov, Fuzzy reliability model of systems for decision support in technical diagnostics. Fuzzy Technologies in the Industry-FTI 2018. Proceedings of the II International Scientific and Practical Conference 222-234 (2018) [Google Scholar]

[16] M.Shcherbakov. V. Kamaev, A. Brebels et al. Outlier detection and classification in sensor data streams for proactive decision support systems, Journal of Physics: Conference Series 803, 1. 012143 (2017) [Google Scholar]