Применение расширенного фильтра Калмана для идентификации параметров распределенной

Authors

DOI:

https://doi.org/10.17308/sait.2018.3/1229

Keywords:

spatially distributed processes, parameter estimation, LSM, multidimensional autoregression, extended Kalman filter

Abstract

A combined method for identifying the equations of mathematical physics describing the dynamics of spatially distributed processes is proposed on the basis of experimental multidimensional time series. The first component of the method is the derivation of OLS estimators of multidimensional autoregression parameters. However, these estimates are biased due to the presence of errors in the regressors. In order to reduce this displacement, a dilated Kalman filter is used as the second component of the method. A computational experiment confirming the effectiveness of the proposed method is given.

Author Biographies

  • А В Копытин, Voronezh State University

    Ph. D. of Рhysical and Мathematical Sciences, Associate Professor, Department of Information Technologies in Management, Computer Sciences Faculty, Voronezh State University

  • Е А Копытина, Voronezh State University

    Postgraduate Student, Department of Information Technologies in Management, Computer Sciences Faculty, Voronezh State University

  • М Г Матвеев, Voronezh State University

    Ph. D. of Technical Sciences, Full Professor, Head of Department of Informa-tion Technologies in Management, Computer Sciences Faculty, Voronezh State University

References

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Published

2018-09-30

Issue

No. 3 (2018): July - September

Section

Mathematical Methods of System Analysis and Management

License

Условия передачи авторских прав in English

How to Cite

Применение расширенного фильтра Калмана для идентификации параметров распределенной. (2018). Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies, 3, 44-50. https://doi.org/10.17308/sait.2018.3/1229

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