Yu.I. Nechaev - Dr. Sc. (Eng.), Professor, ITMO University (Saint Petersburg). E-mail: nechaev@ifmo.mail.ru

Expansion of functionality of use of intelligence technologies at the control transport and social disasters is connected with perfection of methods of processing of the information in complex dynamic environments. The developed conceptual decisions at creation of virtual testbed (VT) monitoring of the catastrophic phenomena are realized in the form of the integrated multiprocessing computer complex. The complex functions on the basis of dynamic model of catastrophe and includes two basic functional blocks - graph-analytic (GA system) and neuro-dynamic systems (ND system). The dynamic model of catastrophe at the control of extreme situations in the transport and social environment opens prospects of construction of algorithms and the software of applied problems at intellectual support of the centre of the competence in various areas of practical appendices. Used in ВП the axiomatic basis realizes a chain of transformation of the information, connecting the topological analysis with synthesis of VT on the basis of formal models of the theory of dynamic systems and intelligence technologies. Realization of a paradigm of high-performance computing is carried out on the basis of integration of technologies of cloudy model and grid-system. The cloudy-model represents the integrated complex providing the organization of the computing environment at level of an information, functional and hardware configuration. The problems solved within the limits of grid-systems, on the character appear close to problems of interpretation of TS systems on the basis of methods of the modern theory of catastrophe and intelligence technologies. The cloud-model concept provides the organization of hardware-software complex ВП at firm-developer level, and Grid-systems - sharing of resources and supporting toolkit of the organizations-co-authors. ВП represents the difficult computer complex realizing iterative process of formalization of knowledge at functioning of TS systems at various level of operating indignations. Feature of this complex consists in integration of knowledge of features of interaction of dynamic objects (DO) investigated TS system. Evolution of TS systems is displayed on a basis of synergetic paradigms. Within the limits of such interpretation the approach realized with use of the generalized of dissipation principle, the formulated N.N.Moiseev is used. This principle has universal character and can be used at studying of behavior of TS systems. The theoretical base of creation ВП is formed on the basis of an effective combination of the saved up system of knowledge to new approaches and artificial intelligence paradigms. Complex realization is carried out on the basis of a principle of an openness, a principle of nonlinear self-organizing, a principle of complexity and the uncertainty factor, and also a principle of normal functioning within the limits of a hypothesis quasi-stationary. The management and decision-making purpose at VT functioning is achievement desirable (target) аttractor, i.e. asymptotic a steady final condition of TS systems. Dimension target attractor it is essential less dimension of initial space of controllable environments. It has allowed to formulate the statements defining the general approach to modeling of behavior of objects of TS systems in frameworks of a synergetic paradigm. As follows from theoretical principles of the modern theory of catastrophe, evolution of TS systems is interpreted in the form of two limiting cases of interaction on a basis фрактальной geometry. The first case characterizes attraction area at system movement to target attractor, the second - area of loss of stability (accident occurrence). The developed structures pf fractal geometry allow to model evolution of TS systems according to noted features of VT functioning at stabilization of a situation in the course of system movement to target attractor (a stable condition) and at loss of stability of system (catastrophe occurrence).

 

1. Bortovye intellektualnye sistemy. CH.1. Aviacionnye sistemy. CH.2. Korabelnye sistemy (M.: Radiotekhnika. 2006). CH. 3. Sistemy korabelnojj posadki letatelnykh apparatov. (M.: Radiotekhnika. 2008).
2. Bukhanovskijj A.V., Ivanov S.V., Nechaev JU.I. Modelirovanie nelinejjnykh sistem v slozhnykh dinamicheskikh sredakh. SPb.: Izd-vo Centr NIU ITMO. 2013.
3. Kolmogorov A.N. O predstavlenii nepreryvnykh funkcijj neskolkikh peremennykh v vide superpozicii nepreryvnykh funkcijj odnogo peremennogo i slozhenija // DAN SSSR. 1957. T. 114. № 5. S. 953−956.
4. Lazarev V.L. Teorija ehntropijjnykh potencialov. SPb.: Izd‑vo Politekhnicheskogo universiteta. 2012.
5. Moiseev N.N. Izbrannye trudy. M.: Tajjreks Ko. 2003.
6. Nejjrokomjutery v intellektualnykh tekhnologijakh KHKHI veka. M.: Radiotekhnika. 2012.
7. Nechaev JU.I., Bukhanovskijj V.V., Vasilev V.N. Koncepcija i metodologicheskie osnovy sozdanija intellektualnogo bazisa grid-sistem // Nauchno-tekhnicheskijj vestnik. SPb GU ITMO. 2008. № 54. S. 13−28.
8. Nechaev JU.I. Teorija katastrof: sovremennyjj podkhod pri prinjatii reshenijj. SPb.: Art-EHkspress. 2011.
9. Nechaev JU.I. Konceptualnye osnovy realizacii nejjronechetkikh sistem pri issledovanii ehkstremalnykh situacijj metodami teorii katastrof v srede «oblachnojj» modeli // Nejjrokompjutery: razrabotka, primenenie. 2012. № 11. S. 3−13.
10. Nechaev JU.I. Principy nejjrodinamiki v zadachakh adaptivnojj identifikacii i prognoza na osnove metodov teorii katastrof v srede «oblachnojj» modeli // XVI Vseros. nauchno-tekhnich. konf. «Nejjroinformatika-2014». Lekcii po nejjroinformatike. M.: MIFI. 2014. S. 199−246.
11. Sinergeticheskaja paradigma. Mnogoobrazie poiskov i podkhodov. M.: Progress-Tradicija. 2000.
12. Sistemy iskusstvennogo intellekta v intellektualnykh tekhnologijakh KHKHI veka. SPb.: Art-EHkspress. 2011.
13. Solodovnikov V.V., Tumarkin V.I. Teorija slozhnosti i proektirovanie sistem upravlenija. M.: Nauka. 1990.
14. Bogdanov A.V., Degtyarev A.B., Nechaev Yu.I. Parallel algorithms for virtual testbed // Proceedings of International conference CSIT-2005. Yerevan, Armenia, 2005. P. 393−403.
15. Defining the Cloud Computing Framework // Cloud Computing Jornal [ehlektronnyjjresurs]: http://cloudcomputing,sys-con.com/node/811519.
16. GTSI Cloud Computing Maturity Model [ehlektronnyjjresurs]: http://www.gtsi.com/sms/documents/White-Papers/Cloud-Computing.pdf.
17. Zadeh L.A. Fuzzy logic, neural networks and soft computing // Sommutation on the ASM-1994. V. 37. № 3. P. 77−84.