Moscow, Russian Federation
St. Petersburg, Russian Federation
Moscow, Russian Federation
BISAC MAT029000 Probability & Statistics / General
The article deals with the issues of developing an axiomatic basis and interpreting conflict situations in conditions of high uncertainty based on the dynamic theory of catastrophes. Control over conflict situations is provided using applied modeling at the expense of the supercomputer center through system integration of technologies and tools for processing large amounts of current information. Functional components of the center for applied simulation implement dynamic visualization and development of management decisions. The key factor in ensuring the safety of critical facilities in a complex conflict situation is the speed of assessment of the situation and the development of adequate management decisions for the implementation of the response. Adequate management is based on experience, as a rule, obtained experimentally in the course of physical modeling of impacts (exercises, trainings, experiments, etc.), and accumulated in the form of a knowledge base of the information and analytical decision support system of the center for applied simulation.
basis, axiomatics, axiomatic basis, interpretation, interpretation methods, conflict situations, urgent calculations
1. Barseghyan A. A., Kupriyanov M. S. Stepanenko V. V., Kholod I. I. Methods and models of data analysis: OLAPand Data Data Mining. Saint-Petersburg: BHV-Petersburg, 2004. 336 p. (in Russian)
2. Tikhonov A. N., Arsenin V. Ya. Methods of solving ill-posed problems, Moscow: Nauka, 1979, 284 p.
3. Nechaev Yu. I. Theory of catastrophes: a modern approach to decision-making. - Saint Petersburg: Art-Express, 2011, 391 p.
4. Nechaev Yu. I. Topology of nonlinear non-stationary systems: theory and applications. Saint Petersburg: Art-Express, 2015, 325 p.
5. Baluta V. I., Osipov V. P., Chetverushkin B. N., Yakovenko O. Yu. Adaptation of intellectual agents in a neoconflict environment / / SCVRT2019 Proceedings of the International scientific conference. 2019. Pp. 28-35.
6. A.Kh. Khakimova, O.V. Zolotarev, M.A. Berberova. Visualization of bibliometric networks of scientific publications on the study of the human factor in the operation of nuclear power plants based on the bibliographic database Dimensions. Scientific Visualization, 2020, volume 12, number 2, pages 127 - 138, DOI: 10.26583/sv.12.2.10, E-ISSN:2079-3537.
7. M.A.Berberova, S.S.Zolotarev, «NPP risk assessments results dependence study on the composition of the population living around the NPP (on the example of Rostov and Kalinin NPP)», GraphiCon 2019 Computer Graphics and Vision. The 29th International Conference on Computer Graphics and Vision. Conference Proceedings (2019), Bryansk, Russia, September 23-26, 2019, Vol-2485, urn:nbn:de:0074-2485-1, ISSN 1613-0073, DOI: 10.30987/graphicon-2019-2-285-289, http://ceur-ws.org/Vol-2485/paper66.pdf, p. 285-289.
8. M.A.Berberova, K.I.Chernyavskii, «Comparative assessment of the NPP risk (on the example of Rostov and Kalinin NPP). Development of risk indicators atlas for Russian NPPs», GraphiCon 2019 Computer Graphics and Vision. The 29th International Conference on Computer Graphics and Vision. Conference Proceedings (2019), Bryansk, Russia, September 23-26, 2019, Vol-2485, urn:nbn:de:0074-2485-1, ISSN 1613-0073, DOI: 10.30987/graphicon-2019-2-290-294, http://ceur-ws.org/Vol-2485/paper67.pdf, p. 290-294.
9. The synergetic paradigm. Variety of searches and approaches. Moscow: Progress-Traditsiya Publ., 2000. 535 p.
10. Sevryugin N. N., Yudin A.V., Kuznetsov A.V. About the methodology of choosing technical solutions / / automation and modern technologies. 2005. no. 3, pp. 27-30.
11. Casti Jnr. Big systems: connectivity, complexity and catastrophes. Moscow: Mir, 1982, 216 p.
12. Smolentsev, S. V. Application of dynamic semantic network for identification in intelligent measurement systems // Collection of reports of the International conference on soft computing and measurement SCM-2000. Saint Petersburg: 2000. vol. 2, pp. 82-83.
13. Tikhomirov V. A., Tikhomirov V. T., Makushkin A.V. the Principle of constructing an information-probabilistic method for implementing a long-term forecast // Software products and systems. No. 2. 2004, pp. 10-15.
14. Kolmogorov A. N. Information theory and theory of algorithms. - M.: Nauka, 1987, 304 p.
15. Solodovnikov V. V., Tumarkin V. I. Theory of complexity and design of control systems. Moscow: Nauka, 1990, 168 p.
16. Lazarson E. V. Modern technology of automated solution of multivariate problems // Automation and modern technologies. 2009. No. 11, pp. 23-29.
17. Golitsin G. A., Petrov V. M. Harmony and algebra of the living. - Moscow: Znanie, 1990, 128 p.
18. Gubko M. V. Mathematical models of optimization of hierarchical structures, Moscow: LENAND, 2006. 264 p.
19. Mesarovich M., Takahara Ya. Obshchaya Teoriya sistem: Matematicheskie osnovy [General theory of systems: mathematical foundations], Moscow: Mir, 1978, 312 p.
20. Moiseev N. N. Selected works, M. TyRex Co., 2003, 376 p.
21. Nikolis J. (Ed.) Dynamics of hierarchical systems: an evolutionary view, Moscow: Mir publ., 1989, 488 p.
22. Figueira G., Almada-Lobo B. Hybrid simulation-optimization methods: A taxonomy and discussion // Simulation Modelling Practice and Theory. - 2014. - T. 46. - S. 118-134.
23. Foster I., Zhao Y., Raicu I., Lu S. Cloud Computing and Grid Computing 360-Degree Compared // eprint arXiv:0901.0131, 2008 [Electronic resource]: http://arxiv.org/ftp/arxiv/papers/0901/0901.0131.pdf
24. E. Gallopoulos E.N. Houstis and J.R. Rice, «Computer as Thinker/Doer: Problem-Solving Environments for Computational Science» IEEE Computational Science & Eng., Vol. 1, No. 2, Summer 1994, pp. 11-23.
25. Szalay A. Extreme data-intensive scientific computing // Computing in Science & Engineering. -2011. - T. 13. - No. 6. - SPp. 34-41.
26. Zadeh L. Fuzzy logic, neural networks and soft computing // Sommutation on the ASM-1994. Vol.37. № 3, p.p.77-84.
