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Russian Physics Journal

, Volume 61, Issue 3, pp 595–601 | Cite as

Recognition of Stochastic System States for Continuous-Discrete Observations with Sliding Memory

  • S. V. Rozhkova
  • V. I. Rozhkova
  • S. P. Moiseeva
  • M. Pagano
MATHEMATICAL PROCESSING OF PHYSICS EXPERIMENTAL DATA

The paper describes the problem of finding the likelihood ratio for specific problem of stochastic system recognition in continuous time to member functions within continuous-discrete time, which depend not only on current, but also on arbitrary numbers of previous non-observable process values.

Keywords

stochastic systems memory recognition likelihood ratio 

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References

  1. 1.
    S. P. Moiseeva and R. T. Yakupov, Russ. Phys. J., 44, No. 6, 583–587 (2001).CrossRefGoogle Scholar
  2. 2.
    S. P. Moiseeva and R. T. Yakupov, Russ. Phys. J., 44, No. 1, 14–17 (2001).CrossRefGoogle Scholar
  3. 3.
    A. A. Nazarov and A. N. Moiseev, Russ. Phys. J., 57, No. 7, 984–990 (2014).CrossRefGoogle Scholar
  4. 4.
    S. P. Sushchenko and R. Tkachev, Izv. Vyssh. Ucebn. Zaved. Fiz., 58, No. 11/2, 220–224 (2015).Google Scholar
  5. 5.
    A. E. Kononyuk, General theory of Recognition, Vol. 1, Basics [in Russian], Osvita Ukrainy, Kiev (2013).Google Scholar
  6. 6.
    D. Middleton, Introduction to Statistical Communication Theory, Mc Graw-Hill, New York (1960).MATHGoogle Scholar
  7. 7.
    A. P. Sage and J. L. Melse, Estimation Theory with Application to Communication and Control, Mc Graw-Hill, New York (1972).Google Scholar
  8. 8.
    A. S. Willsky and H. L. Jones, IEEE Trans. Autom. Control, AC-21, No. 1, 108–112 (1976).CrossRefGoogle Scholar
  9. 9.
    H. Van Trees, Detection, Estimation and Modulation Theory, Part I, Wiley, New York (2004).MATHGoogle Scholar
  10. 10.
    K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York (1990).MATHGoogle Scholar
  11. 11.
    C. S. Grene, An analysis of the multiple model adaptive control algorithm, Ph. D. Dissertation, Cambridge (1978).Google Scholar
  12. 12.
    M. R. Kadirov and N. S. Demin, Russ. Phys. J., 42, No. 3, 356–361 (1999).CrossRefGoogle Scholar
  13. 13.
    N. S. Demin and M. R. Kadirov, Tomsk State Univ. J., No. 271, 160–164 (2000).Google Scholar
  14. 14.
    N. S. Demin and I. E. Safronova, Tomsk State Univ. J., No. 271, 168–171 (2000).Google Scholar
  15. 15.
    N. S. Demin and S. V. Rozhkova, J. Comput. Syst. Sci. Int., 39, No. 3, 335–346 (2000).Google Scholar
  16. 16.
    N. S. Demin, S. V. Rozhkova, and O. V. Rozhkova, Informatica, 12, No. 2, 263–284 (2001).MathSciNetGoogle Scholar
  17. 17.
    R. S. Liptser and A. N. Shiryaev, Statistics of Random Processes, Springer Verlag, Berlin; Heidelberg (2001).CrossRefMATHGoogle Scholar
  18. 18.
    O. L. Abakumova, N. S. Demin, and T. V. Sushko, Autom. Remote Control, 56, No. 10, 1383–1393 (1995).Google Scholar
  19. 19.
    I. I. Gikhman and A. V. Skorokhod, The Theory of Stochastic Processes, Springer Verlag, Berlin; Heidelberg (2004).CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • S. V. Rozhkova
    • 1
  • V. I. Rozhkova
    • 1
  • S. P. Moiseeva
    • 2
  • M. Pagano
    • 3
  1. 1.National Research Tomsk Polytechnic UniversityTomskRussia
  2. 2.National Research Tomsk State UniversityTomskRussia
  3. 3.University of PisaPisaItaly

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