Using a Tensor Model to Handle Uncertainty in Complex Dynamical Systems

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Resumo

The article is devoted to research related to the processing of uncertainty by means of tensor algebra in complex dynamical systems. The “smart electronic hitch” system is considered as an example of a complex dynamic system. The use of such a system is especially important when organizing traffic in difficult conditions. The solution of this problem occurs under conditions of uncertainty that may appear at different levels of the traffic management process. To identify and study in detail the properties of uncertainty, the author suggests using a tensor model. The tensor model makes it possible to identify additional properties of uncertainty, the study of which is not available when using traditional formalisms to represent uncertainty. Using the tensor model allows us to study the spatial model of uncertainty, real and imaginary values of uncertainty, as well as uncertainty invariants with respect to various transformations of the coordinate system. The article proposes a classification of uncertainty in a complex system. Using the example of the organization of interaction of “smart” controllers in an electronic coupling, the author shows the results of applying tensor analysis methods of networks to obtain a computational base of an electronic coupling. Tensor equations provide efficient processing of big data, obtaining information in real time, the stability of the dynamic system to changes in the topology of the connection of controllers and changes in the soft and hard components of the connection. The results obtained in the article show that the tensor uncertainty model can be successfully implemented in a dynamic system of any complexity level.

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Sobre autores

Alexandra Volosova

Moscow Automobile and Road State Technical University (MADI)

Autor responsável pela correspondência
Email: volosova_av@madi.ru
ORCID ID: 0000-0002-3817-2671
Scopus Author ID: 57437351400
Researcher ID: T-1829-2017

Candidate of Engineering, Associate Professor; associate professor at the Moscow Automobile and Road State Technical University (MADI)

Rússia, Moscow

Bibliografia

  1. Volosova A.V. Uncertain knowledge representation by means of tensor algebra. Computational Nanotechnology. 2019. No. 1. Pp. 60–64.
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  3. Matiukhina E.N., Maksimychev O.I., Ostroukh A.V., Vasiliev Y.E. Connected vehicle remote diagnostic system. In: Systems of signals generating and processing in the field of on board communications. Conference Proceedings. 2021. 9416001.
  4. Matyukhina E.N., Kuftinova N.G., Ostroukh A.V. et al. Hybrid smart systems for big data analysis // Russian Engineering Research. 2021. No. 41 (6). Pp. 536–538.
  5. Ostroukh A.V., Kuftinova N.G., Gaevskii V.V. et al. Digital transformation of enterprises using a low-code platform. Russian Engineering Researchthis Link is Disabled. 2022. No. 42 (11). Pp. 1203–1206.
  6. Subbotin B.S., Ahmetzhanova E.U. Research on the integration of intelligent transport systems. IOP Conference Series: Materials Science and Engineering. 2019. Vol. 832.
  7. Vasiliev E., Fineeva M.A., Belyakov A.B. et al. Decision support system for street-road network objects repair. IOP Conference Series: Materials Science and Engineering. 2021. Vol. 1159 (1). Art. No. 012025.
  8. Volosova A.V., Maksimychev O.I., Ismoilov M.I. et al. Platforms and complexes for unmanned technologies in road transport. In: Intelligent technologies and electronic devices in vehicle and road transport complex. TIRVED 2022. Conference Proceedings. 2022.
  9. Volosova A.V., Maksimychev O.I., Ostroukh A.V. et al. Uncertainty processing by tensor algebra means in condition of movement along complex roads. In: Systems of signals generating and processing in the field of on board communications. SOSG 2022. Conference Proceedings. 2022.
  10. Volosova A.V., Pronin C.B., Maksimychev O.I. et al. Creating quantum circuits for training perceptron neural networks on the principles of grover’s algorithm. In: Systems of signals generating and processing in the field of on board communications. SOSG 2022. Conference Proceedings. 2022.
  11. Volosova A.V., Kuftinova N.G., Maksimychev O.I. et al. Data fabric as an effective method of data management in traffic and road systems. In: Systems of signals generating and processing in the field of on board communications. SOSG 2022. Conference Proceedings. 2022.
  12. Volosova A.V., Ostroukh A.V., Pronin T.B. et al. Hyperautomation in the auto industry. Russian Engineering Researchthis Link is Disabled. 2021. No. 41 (6). Pp. 532–535.
  13. Volosova A., Matyukhina E., Akimov D. The use tensor method of dual networks for analysis of the transport and tourist components of sustainable development of territories. E3S Web of Conferences. 2020. Vol. 208. 05012.
  14. Volosova A., Matiukhina E. Using artificial intelligence for effective decision-making in corporate governance under conditions of deep uncertainty. SHS Web of Conf. 2020. Vol. 89. 03008.
  15. Volosova A.V., Yurchik P.F., Maksimychev O.I., Golubkova V.B. Tensor analysis of uncertainty in freight transport ULS-systems. In: IOP Conference Series. Materials Science and Engineering.

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2. Fig 1. Fuzzy variable 2068.9 (а) and its tensor (b)

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3. Fig 2. Concomitant tensors of a fuzzy variable 2068.9 а – ball tensor (particular properties of variable); b – deviator (the amplitude of fluctuations in the values of the variable)

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4. Fig 3. Symmetric (а) and antisymmetric (b) tensors for a fuzzy variable 2068.9

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5. Fig 4. Complex values of a fuzzy variable

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6. Fig 5. The intelligent electronic hitch for traffic control

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7. Fig 6. Example of uncertainty processing by an expert module

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8. Fig 7. An example of using a tensor model to handle uncertainty

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