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Nonlinear parametric identification of stochastic discrete plants based on generalized probabilistic criteria

  • Control in Stochastic Systems and Under Uncertainty Conditions
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Abstract

Studying alternative (with respect to conventional) methods of stochastic parameter identification of nonlinear discrete plants is shown to be of current interest. For the first time, generalized probabilistic criteria for discrete time are proposed to solve the problem of parametric identification of nonlinear stochastic plants. A parametric identification algorithm is proposed based on the criterion of the minimum probability of estimation error. A toy example is considered to illustrate the efficiency of the proposed approach.

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Authors and Affiliations

  1. Rostov State Transport University, pl. Rostovskogo Strelkovogo Polka Narodnogo Opolcheniya 2, Rostov-on-Don, 344038, Russia

    P. A. Kucherenko & S. V. Sokolov

Authors
  1. P. A. Kucherenko
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  2. S. V. Sokolov
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Original Russian Text © P.A. Kucherenko, S.V. Sokolov, 2011, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2011, No. 6, pp. 28–37.

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Kucherenko, P.A., Sokolov, S.V. Nonlinear parametric identification of stochastic discrete plants based on generalized probabilistic criteria. J. Comput. Syst. Sci. Int. 50, 884–892 (2011). https://doi.org/10.1134/S1064230711050133

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  • DOI: https://doi.org/10.1134/S1064230711050133

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