Просматривается номер   2019-69-4
To the readers
About journal
Publication Ethics
Requirements for papers
Topics
Review Process
English (United Kingdom)Russian (CIS)
Information technology in system analysis
A.L. Tsaregorodtsev, S.S. Slobodenyuk, S.V. Voloshin, A.V. Vokhmintcev An approach to the storage and processing of original semi-structured geological and geophysical information
N.I. Yusupova, O.N. Smetanina, E.Yu. Sazonova, A.I. Agadullina, T.V. Naumova Data Mining decision support on improvement a person’s psychophysical readiness for successful professional activity
Methods and models of system analysis
O.S. Nurgayanova, N.I. Yussupova Training a neural network to predict the properties of nickel alloys with a genetic algorithm
S.V. Kostyuchenko, N.A. Cheremisin, A.N. Emelyanov Localization of current recoverable oil reserves and direct calculation of displacement coverage in geological and technological models of nonlinear filtration
NONLINEAR DYNAMICAL SISTEMS
S.N. Shergin, S.G. Pyatkov, E.I. Safonov Numerical solution inverse problems for equation of quasistationary electromagnetic waves in anisotropic nonmetallic media
E.I. Safonov Algorithm of recovering a point source in a one-dimensional heat equation
V.V. Rotko On some classes of inverse problems for convection-diffusion systems and their generalizations
Risk management and safety
M.B. Guzairov, A.M. Vulfin, V.M. Kartak, A.D. Kirillova, K.V. Mironov Comparative analysis of algorithms for cognitive modeling in assessing information security risks
V.I. Vasilyev, A.M. Vulfin, V.M. Kartak, A.D. Kirillova, K.V. Mironov System of attacks detection in wireless sensor networks of Industrial Internet of Things
S.V. Smirnov Fuzzy cognitive maps formation based on ontological analysis of expert data
S.V. Smirnov Fuzzy cognitive maps formation based on ontological analysis of expert data

Abstract.

The problem of constructing fuzzy cognitive maps is considered. The task is to overcome the incompleteness and inconsistency of engaged experts opinions regarding the concepts composition of the cognitive map and their mutual influence. To solve the problem, it is proposed to apply models and methods of ontological data analysis. The expertise data is structured in the form of a correspondence “ordered pairs of concepts - influences”: the concepts specified by at least one expert are taken into account, and the opinion of each expert about the influence of the first concept on the second is fixed. Opinion is expressed by selecting
an element from a finite set of linguistic constants, which reflect the realities of expert data. Weighing and integrating of the experts opinions using the methods of multi-valued vector logic allows us to form a non-strict correspondence “ordered pairs of concepts - influences”. A technique is proposed for converting this non-strict correspondence into a self-consistent fuzzy one. The main role here is given to the method of rational threshold section of non-strict correspondence developed in the framework of ontological analysis of data. This method is able to take into account the existence constraints of different types of influence for each pair of map concepts. Finally, the aggregated influence weights are defuzzified by the well-known center of gravity method.

Keywords:

fuzzy cognitive map, expert data, ontological data-analysis, properties existence constraints

PP. 79-86.

DOI: 10.14357/20790279190410

References

1. Axelrod, R. 1976. Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press. 395 p.
2. Kosko, B. 1986. Fuzzy cognitive maps. International Journal of Man-Machine Studies. 24(1): 65-75.
3. Papageorgiou, E. and J. Salmeron. 2013. A Review of Fuzzy Cognitive Map Research at the Last Decade. IEEE Transactions on Fuzzy Systems. 21(1): 66-79.
4. Glykas, M. 2010. Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications (Introduction). 247. Springer-Verlag Berlin  Heidelberg. IX-XV.
5. Borisov, V.V., V.V. Kruglov, and A.S. Fedulov. 2007. Nechetkiye modeli i seti [Fuzzy Models and Networks]. Moscow: Hot Line – Telecom Publs. 284 p.
6. Kuznetsov, O.P., A.A. Kulinich, and A.V. Markovsky. 2006. Analiz vliyaniy pri upravlenii slabostrukturirovannymi situatsiyami na osnove kognitivnykh kart. [Analysis of influences in the management of poorly structured situations based on cognitive maps]. In: Abramova N.A., K.S. Ginsberg, and D.A. Novikov (Eds.). The human factor in management. Moscow: KomKniga Publs. 313-344.
7. Aguilar, J. 2005. A survey about fuzzy cognitive maps papers (Invited paper). Int. J. Computational Cognition. 3(27): 27-33.
8. Groumpos, P.P. 2018. Intelligence and Fuzzy Cognitive Maps: Scientific Issues, Challenges and Opportunities. Studies in Informatics and Control. 27(3): 247-264.
9. Kulinich, A.A. 2010. Komp’yuternyye sistemy modelirovaniya kognitivnykh kart: podkhody i metody [Software for modeling cognitive maps: approaches and methods]. Problemy upravleniya [Control science]. 3: 2-16.
10. Stach, V., L. Kurgan and W. Pedrycz. 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. 247: 23-41.
11. Abramova, N.A. and S.V. Kovriga. 2008. Nekotoryye kriterii dostovernosti modeley na osnove kognitivnykh kart. [Some criteria of reliability of models based on cognitive maps]. Problemy upravleniya [Control science]. 6: 23-33.
12. Schneider, M., E. Shnaider, A. Kandel, and G. Chew. 1998. Automatic construction of FCMs. Fuzzy Sets System. 93(2): 161-172.
13. Stylios, D.C., and P.P. Groumpos. 2004. Modeling Complex Systems Using Fuzzy Cognitive Maps. IEEE Transaction on Systems, Man and Cybernetics. Part A: Systems and Humans. 34: 155-162.
14. Korostelev, D.A., D.G. Lageryov and A.G. Podvesovsky. 2008. Sistema podderzhki prinyatiya resheniy na osnove nechetkikh kognitivnykh modeley “IGLA” [Decision support system based  on fuzzy cognitive models “IGLA”]. Trudy XI natsional’noy konferentsii po iskusstvennomu  intellektu KII-2008 [Proc. of the XI national conf. on artificial intelligence KII-2008] (September 28-October 03, 2008, Dubna, Russia). Vol. 3.  Moscow: LENAND Publs. 329-336.
15. Vittikh, V.A. 2015. Introduction to the Theory of  Intersubjective Management. Group Decision and Negotiation. 24(1): 67-95.
16. Smirnov, S.V. 2017. Modeli i metody formirovaniya kognitivnykh kart pri ikh kollektivnoy razrabotke [Models and methods for the formation of cognitive maps in collective development] Trudy VI mezhdunarodnoy nauch. konf. “Informatsionnyye tekhnologii i sistemy ITiS-2017” [Proc. of the VI Int. scientific conf. “Information Technologies and Systems ITiS- 2017”] (March 1-5, 2017, Bannoe, Russia). Eds.: Popkov Yu.S., and A.V. Melnikov. Chelyabinsk State University. 281-283.
17. Smirnov, S.V. 2014. Mnogoznachnaya i nechetkaya logika v ontologicheskom analize dannykh [Multi-valued and fuzzy logic in ontological data analysis]. Trudy III mezhdunarodnoy nauch. konf. “Informatsionnyye tekhnologii i sistemy ITiS-2014” [Proc. of the III Int. scientific conf. “Information Technologies and Systems ITiS-2014”] (February 26 - March 2, 2014, Bannoe, Russia). Eds.: Popkov Yu.S., and A.V. Melnikov. Chelyabinsk State University. 90-91.
18. Kovartsev, A.N., V.S. Smirnov, and S.V. Smirnov. 2015. Intellektualizatsiya formirovaniya konteksta dlya vyvoda ponyatiynoy struktury predmetnoy oblasti [Context formation for the conceptual structure elicitation of the subject area]. Trudy IV mezhdunarodnoy nauch. konf. “Informatsionnyye tekhnologii i sistemy ITiS-2015” [Proc. of the IV Int. scientific conf. “Information Technologies and  Systems ITiS-2015”] (February 25 - March 1, 2015, Bannoe, Russia). Eds.: Popkov Yu.S., and A.V. Melnikov. Chelyabinsk State University. 82-83.
19. Ofiсerov, V.P., and S.V. Smirnov. 2017. Fuzzy formal concept analysis in the construction of ontologies. Ontologiya proyektirovaniya [Ontology of Designing]. 7(4): 487-495.
20. Ganter, B., and R. Wille. 1989. Conceptual scaling. In: F. Roberts (Ed.): Applications of Combinatorics and Graph Theory to the Biological and Social Sciences. – N.Y.: Springer-Verlag. 139-167.
21. Samoilov, D.E., V.A. Semenova, and S.V. Smirnov. 2018. Fraktal’nost’ ogranicheniy sosushchestvovaniya svoystv v zadachakh mashinnogo obucheniya [Fractality of the object’s properties existence constraints in machine learning]. Sbornik trudov IV mezhdunarodnoy konferentsii i molodezhnoy shkoly «Informatsionnyye tekhnologii i nanotekhnologii» ITNT-2018 [Proc. of the IV international conference and youth school “Information Technologies and Nanotechnologies” ITNT-2018] (April 24-27, 2018, Samara, Russia). Samara: New Technique Publs. 2512-2518.
22. Lammari, N., and E. Metais. 2004. Building and maintaining ontologies: a set of algorithms. Data & Knowledge Engineering. 48(2): 155-176.
23. Smirnov, S.V. 2017. Biconstituent phenomenon of information and cognitive data analysis. Procedia Engineering. 201: 773-778.
24. Arshinsky, LV. 2007. Vektornyye logiki: osnovaniya, kontseptsii, modeli [Vector Logics: Foundations, Concepts, and Models]. Irkutsk: Irkutsk State University. 228 p.
25. Silov, V.B. 1995. Prinyatiye strategicheskikh resheniy v nechetkoy obstanovke [Strategic decisions making in a fuzzy environment]. Moscow: INPRO-RES Publs. 228 p.
 
     
2024-74-1
2023-73-4
2023-73-3
2023-73-2

© ФИЦ ИУ РАН 2008-2018. Создание сайта "РосИнтернет технологии".