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Cybernetics And Systems Analysis
International Theoretical Science Journal
UDC 519.7
V.F. Gubarev1


1 Space Research Institute of the National Academy of Sciences
of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine

v.f.gubarev@gmail.com

PROBLEMS OF MATHEMATICAL DATA INTERPRETATION.
II. DISTRIBUTED PARAMETER SYSTEMS

Abstract. The problem of mathematical interpretation of experimental data is considered for distributed parameter system with the use of models supposed to be adequate to the objects under study. For linear systems, on the basis of Green functions, theoretical foundations are developed, which allow setting different inverse problems associated with the interpretation problem. Many of them are treated as ill-posed. So, regularized procedures that make it possible to find approximate solutiond consistent with errors in available data are recommended and described. In this connection, representation of the model class in the form of expansionsthat asymptotically approach the exact description is important. Constructive algorithms to solve interpretation problem are given.

Keywords: interpretation problem, assimilation data, inverse problems, distributed systems, regularization, identification, asymptotic models.



FULL TEXT

REFERENCES

  1. Gubarev V.F. Problem of mathematical data interpretation. I. Systems with lumped parameters. Kibernetika i sistemnyj analiz. 2019. Vol. 55, N 2. P. 59–72.

  2. Butkovsky A.G. Characteristics of systems with distributed parameters [in Russian]. Moscow: Nauka, 1979. 224 p.

  3. Andreev Yu.N. Finite-dimensional linear objects control [in Russian]. Moscow: Nauka, 1976. 424 p.

  4. Tikhonov A.N., Arsenin V.Ya. Methods for solving incorrect problems [in Russian]. Moscow: Nauka, 1979. 285 p.

  5. Golub J., Van Loan C. Matrix calculations [Russian translation]. Moscow: Mir, 1999. 548 p.

  6. Gokhberg I.Ts., Krein M.G. Introduction to the theory of linear non-self-adjoint operators in a Hilbert space [in Russian]. Moscow: Nauka, 1965.

  7. Gubarev V.F. Rational approximation of systems with distributed parameters. Kibernetika i sistemnyj analiz. 2008. N 2. P. 99–116.

  8. Glower K., Curtain R.F., Partington J.R. Realization and approximation of linear infinite-dimensional systems with error bounds. SIAM J. Control and Optimization. 1988. Vol. 26, N 4. P. 863–898.

  9. Verhaegen M., Dewilde P. Subspace model identification. Part 1: The output-error state space model identification class of algorithms. International Journal of Control. 1992. Vol. 56, N 5. P. 1187–1210.

  10. Van Overschee P., De Moor B. Subspace identification for linear systems. Boston; London; Dordrecht: Kluwer Academic Publishers, 1996. 254 p.

  11. Viberg M. Subspace-based methods for the identification of linear time-invariant systems. Automatica. 1995. Vol. 31, N 12. P. 1835–1851.

  12. Gubarev V.F., Romanenko V.D., Miliavskyi Yu.L. Methods of finding regularized solution in identification of linear multivariable multi-connected discrete systems. Kibernetika i sistemnyj analiz. 2019. Vol. 55, N 6. P. 3–16.

  13. Peddie N.W. Current loop models of the Earth’s magnetic field. Journal Geophys. Res. 1979. Vol. 84. P. 4517–4523.

  14. Borisenko A.I., Taranov I.E. Vector analysis and the beginning of tensor calculus [in Russian]. Moscow: Vyssh. shk., 1963. 263 p.

  15. Gubarev V.F. Assessment of substitution currents in the environment and plasma of tokamak installations. Problemy upravleniya i informatiki. 1995. N 4. P. 74–80.

  16. Nepoklonov V.B., Lidovskaya E.A., Kapranov Yu.S. Assessment of the quality of the Earth's gravitational field models. Izvestiya Vuzov. Geodeziya i aerofotos’yemka. 2014. N 2. P. 24–32.
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