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Vestnik YuUrGU. Ser. Mat. Model. Progr., 2020, Volume 13, Issue 2, Pages 144–150 (Mi vyuru551)  

Short Notes

Convergence analysis of the guaranteed parameter estimation algorithm for models of one-dimensional chaotic systems

A. S. Sheludko

South Ural State University, Chelyabinsk, Russian Federation

Abstract: This paper considers the parameter estimation problem for models of one-dimensional chaotic systems. The guaranteed algorithm is proposed in the context of set-membership approach, which assumes that only intervals of possible values are known for the uncertain variables in the model (initial condition, parameter and measurement errors). The algorithm recursively computes the interval estimates of the parameter at every time step. If the prior information is correct, found interval estimates always contain the true value of the parameter. For certain models of measurement errors the result of the algorithm is the exact value of the parameter (the final interval estimate contains a single point). The goal of this study is to derive conditions under which the guaranteed algorithm improves the interval estimate of the parameter.

Keywords: chaotic system, nonlinear model, parameter estimation, interval estimate.

Funding Agency Grant Number
Ministry of Education and Science of the Russian Federation 02.A03.21.0011
The work was supported by Act 211 Government of the Russian Federation, contract no. 02.A03.21.0011.


DOI: https://doi.org/10.14529/mmp200213

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UDC: 519.7
MSC: 93E12, 65G40
Received: 07.11.2019
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Citation: A. S. Sheludko, “Convergence analysis of the guaranteed parameter estimation algorithm for models of one-dimensional chaotic systems”, Vestnik YuUrGU. Ser. Mat. Model. Progr., 13:2 (2020), 144–150

Citation in format AMSBIB
\Bibitem{She20}
\by A.~S.~Sheludko
\paper Convergence analysis of the guaranteed parameter estimation algorithm for models of one-dimensional chaotic systems
\jour Vestnik YuUrGU. Ser. Mat. Model. Progr.
\yr 2020
\vol 13
\issue 2
\pages 144--150
\mathnet{http://mi.mathnet.ru/vyuru551}
\crossref{https://doi.org/10.14529/mmp200213}


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