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Avtomat. i Telemekh., 2011, Issue 9, Pages 142–160 (Mi at2281)  

This article is cited in 6 scientific papers (total in 6 papers)

Stochastic Systems, Queuing Systems

Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems

Yu. V. Tsyganova

Ul'yanovsk State University, Ul'yanovsk, Russia

Abstract: We consider an application of the auxiliary quality functional (AQF) method for identifying the parameters of a linear dynamical system in case when the filtering is done with a square root covariance filter. We construct a new algorithm for computing the gradient of the auxiliary quality functional. The advantages of this algorithm are that it is stable to computer rounding errors and does not require the user to write down the “differentiated” Kalman filter in the standard form for every unknown system parameter. All values necessary to compute the values of the AQF gradient are computed in terms of the square root covariance filter with orthogonal transformations.

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English version:
Automation and Remote Control, 2011, 72:9, 1925–1940

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Presented by the member of Editorial Board: А. В. Назин

Received: 06.10.2009

Citation: Yu. V. Tsyganova, “Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems”, Avtomat. i Telemekh., 2011, no. 9, 142–160; Autom. Remote Control, 72:9 (2011), 1925–1940

Citation in format AMSBIB
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\by Yu.~V.~Tsyganova
\paper Computing the gradient of the auxiliary quality functional in the parametric identification problem for stochastic systems
\jour Avtomat. i Telemekh.
\yr 2011
\issue 9
\pages 142--160
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\transl
\jour Autom. Remote Control
\yr 2011
\vol 72
\issue 9
\pages 1925--1940
\crossref{https://doi.org/10.1134/S0005117911090141}
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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. Yu. V. Tsyganova, M. V. Kulikova, “On efficient parametric identification methods for linear discrete stochastic systems”, Autom. Remote Control, 73:6 (2012), 962–975  mathnet  crossref  isi
    2. M. V. Kulikova, Yu. V. Tsyganova, “A general approach to constructing parameter identification algorithms in the class of square root filters with orthogonal and $J$-orthogonal tranformations”, Autom. Remote Control, 75:8 (2014), 1402–1419  mathnet  crossref  isi
    3. M. V. Kulikova, J. V. Tsyganova, “Differentiating matrix orthogonal transformations”, Comput. Math. Math. Phys., 55:9 (2015), 1419–1431  mathnet  crossref  crossref  mathscinet  isi  elib  elib
    4. Semushin I. Tsyganova J. Kulikova M. Tsyganov A. Peskov A., “Identification of Human Body Daily Temperature Dynamics Via Minimum State Prediction Error Method”, 2016 European Control Conference (Ecc), IEEE, 2016, 2429–2434  crossref  isi
    5. Kostoglotov A., Deryabkin I., Andrashitov D., Lazarenko S., Pugachev I., “Synthesis of Algorithms For Estimation of Parameters and State of Dynamic Systems Using Additional Invariants”, Proceedings of 2016 IEEE East-West Design & Test Symposium (Ewdts), IEEE, 2016  isi
    6. Yu. V. Tsyganova, A. V. Tsyganov, “O vychislenii znachenii proizvodnykh v LD-razlozhenii parametrizovannykh matrits”, Izvestiya Irkutskogo gosudarstvennogo universiteta. Seriya Matematika, 23 (2018), 64–79  mathnet  crossref
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