|Article title||METHODOLOGY FOR COMPARATIVE COGNITIVE MODELING BASED ON THE ANALYSIS OF FUZZY TARGET AND CONTROL FACTORS|
|Authors||A.N. Tselykh, L.A. Tselykh|
|Section||SECTION III. MODELING AND DESIGN|
|Month, Year||07, 2015 @en|
|Abstract||The article deals with analytical procedures and algorithms in comparative cognitive modeling using fuzzy triangular numbers. The analytical process is demonstrated on the example of solving the problems of designing models of corporate governance based on the dynamic analysis of fuzzy cognitive models in order to effectively identify the internal representations of models (mental representations) and their practical application. In the study cognitive models have been built based on the strategic imperatives of corporate governance, causal and mutual influence (positive and negative) factors-concepts have been identified. The formation of cognitive models that are suitable for comparative analysis is made on the basis of segmentation factors-concepts influence in terms of their spatial and temporal becoming. The analysis of the most important components and relationships between concepts based on the interaction of the target and control factors of influence. Analytical procedures are presented based on the identification of groups of peremptory factors shaping the institutional framework of the system (internal and external), gaining an understanding of system design, the study of the movement of concepts in the evolution process of the system, visualization and interpretation of results. On the basis of mathematical processing and graphical representation of cognitive models analysis of serial sections has been made, which allows to identify the real action vectors of the system. This approach uses visualized cognitive function of the subject of management to illustrate the simulation results. The proposed approach for dynamic analysis of fuzzy cognitive models allows us to trace the evolutionary features of formation and development of the system based on the analysis of the movement of the imperative factors in the developed models systematically influencing the object of study.|
|Keywords||Comparative cognitive modeling; decision support; fuzzy cognitive maps.|
|References||1. Karanashev, A.Kh. Tselykh, L.A., Karasheva, A.G. Metodologicheskie aspekty diagnostiki innovatsionnogo potentsiala korporativnoy kul'tury predpriyatiy rekreatsionnoy sfery Kabardino-Balkarskoy Respubliki: sravnitel'noe kognitivnoe modelirovanie [Methodological aspects of diagnosis of innovative potential of the corporate culture of the enterprises of recreational sphere of the Kabardino-Balkarian Republic: a comparative cognitive modeling], Vestnik Adygeyskogo gos. un-ta [Bulletin of the Adygeya State University], 2014, No. 3 (122), pp. 256-265.
2. Obiedat M., Samarasinghe S. Fuzzy representation and aggregation of fuzzy cognitive maps, 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013, pp. 690-694. Available at: www.mssanz.org.au/modsim2013.
3. Arduin P.E. On the Use of Cognitive Maps to Identify Meaning Variance. Lecture Notes, Business Information Processing, Springer-Verlag, 2014, No. 180, pp. 73-80. Available at:
4. Jones N.A., Ross H., Lynam T., Perez P., Leitch A. Mental models: an interdisciplinary synthesis of theory and methods, Ecol. Soc., 2011, No. 16 (1), pp. 46.
5. Budhwar P.S. Cognitive Mapping as a Tool to Elicit Managerial Cognitions: Methodology Analysed, Vikalpa, 1996, Vol. 21, No. 4, October-December, pp. 17-25.
6. Motlagh O., Jamaludin Z., Tang S.H. & Khaksar W. An agile FCM for real-time modeling of dynamic and real-life systems, evolving systems, Special issue on temporal aspects in fuzzy cognitive maps, 2013. DOI: 10.1007/s12530-013-9077-6.
7. Eden С. Analyzing cognitive maps to help structure issues or problems, European Journal of Operational Research, 2004, No. 159, pp. 673-686.
8. Glykas M. (ed.) Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools and Applications. Springer: Berlin, 2010.
9. Papageorgiou E.I., Salmeron J.L. A Review of Fuzzy Cognitive Map research at the last decade, Fuzzy Systems, IEEE, 2013, Vol. 21, Issue 1, pp. 66-79.
10. Motlagh O., Papageorgiou E.I., Tang S.Y. & Jamaludin Z. Multivariate Relationship Modeling using Nested Fuzzy Cognitive Map (Model Hubungan Multivariasi Menggunakan Peta Kognitif Kabur Tersarang), Sains Malaysiana, 2014, No. 43 (11), pp. 1781-1790.
11. Hobbs B.F., Ludsin, S.A., Knight, R.L., Ryan, P.A., Biberhofer, J., Ciborowski, J.J.H. Fuzzy cognitive mapping as a tool to define management objectives for complex ecosystems, Ecol. Appl., 2002, No. 12, pp. 1548-1565.
12. Stach W., Kurgan L., Pedrycz W. A divide and conquer method for learning large Fuzzy Cognitive Maps, Fuzzy Sets and Systems, 2010, No. 161, pp. 2515-2532.
13. McKay J., Marshall P. Reflecting on the Efficacy of SODA and Cognitive Mapping for Problem Analysis in Information Requirements Determination Judy McKay, Proceedings of the 5th Annual SIG IS Cognitive Research Exchange Workshop (IS CORE), Las Vegas, Nevada, United States, 11 December 2005. Available at: http://www.ou.edu/is-core/Papers/McKay-
14. Maksimov V.I., Kornoushenko E.K., Kachaev S.V. Kognitivnyy analiz i modelirovanie slozhnykh situatsiy [Cognitive analysis and modeling of complex situations], Bankovskie tekhnologii [Banking Technology], 2001, No. 7, pp. 21-26.
15. Prichina O.S. Sistemnyy podkhod v upravlenii kompleksnym razvitiem regiona [System approach in controlling the complex development of the region], Terra Economicus, 2009, Vol. 7, No. 3, pp. 1.
16. Taber R. Knowledge processing with fuzzy cognitive maps, Expert Syst., 1991, Appl 2, pp. 83-87.
17. Tselykh A.N. Razrabotka i issledovanie modeley prinyatiya resheniy v integrirovannykh intellektual'nykh sistemakh i ikh primenenie dlya resheniya ekologicheskikh zadach: dis. … d-ra tekhn. nauk [Development and research of models of decision making in integrated intelligent systems and their applications to solve environmental problems. Dr. of eng. sc. dis.]:
05.13.14, 05.13.16. Taganrog, 2000.
18. Tselykh A.N., Tselykh L.A. Funktsional'naya struktura sistemy izvlecheniya znaniy ekspertnykh sistem, adaptirovannaya dlya resheniya prikladnykh upravlencheskikh zadach [Functional structure of the system of knowledge extraction in expert systems adapted for the solution of applied management problems], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 8 (154), pp. 193-201.
19. Tselykh A.N., Tselykh L.A. Logicheskaya skhema predstavleniya reshaemykh zadach v informatsionnoy sisteme dlya upravleniya biznesom [Logic chart for representing tasks in the information system for business management], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2014, No. 1 (150), pp. 93-99.
20. Tselykh A.N., Kotov E.M. Metody nechetko-mnozhestvennogo analiza i modelirovaniya sotsial'nykh grafov [Methods fuzzy-set analysis and modeling of social graphs], Sovremennye problemy nauki i obrazovaniya [Modern Problems of Science and Education], 2013, No. 6, pp. 98.
21. Tselykh A.N., Tselykh A.A. Pozitsionnyy analiz v sotsial'nykh setyakh na osnove otnosheniya ekvivalentnosti [Positional analysis of social networks based on relations of equivalence], Izvestiya vuzov. Severo-Kavkazskiy region. Estestvennye nauki [Izvestiya Vuzov. SeveroKavkazskii Region. Natural Science], 2011, Special issue, pp. 73-76.