Simplified identification algorithm for classical linear regression containing power functions of unknown parameter
https://doi.org/10.17586/0021-3454-2023-66-6-514-518
Abstract
The classical linear regression equation is considered, containing the measured signal in the left part and the sum of terms consisting of the product of unknown parameters and known functions (regressors) in the right part. A distinctive feature of the considered equation from the classical one is the assumption that the unknown parameters are non-linear combinations of one. Namely, each of the unknown parameters is obtained by raising one unknown parameter to a power. The article proposes a new simplified procedure for searching for the unknown parameter, which, unlike the widely used gradient descent method, allows, on the one hand, to significantly simplify the identification algorithm, and, on the other hand, to expand the assumptions for regressors.
Supporting agencies: The article was prepared with the financial support of the Ministry of Science and Higher Education of the Russian Federation, grant 2019-0898.
About the Authors
V. S. Vorobyev
ITMO University
Russian Federation
Russian Federation
Vladimir S. Vorobev — Post-Graduate Student; ITMO University, Faculty of Control Systems and Robotics.
St. Petersburg
A. A. Bobtsov
ITMO University
Russian Federation
Russian Federation
Alexey A. Bobtsov — Dr. Sci., Professor; ITMO University, School of Computer Technologies and Control, Head of the School; Faculty of Control Systems and Robotics, Professor at the Faculty; International Laboratory of Adaptive and Nonlinear Control Systems, Head of the Lab.
St. Petersburg
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Review
For citations:
Vorobyev V.S., Bobtsov A.A. Simplified identification algorithm for classical linear regression containing power functions of unknown parameter. Journal of Instrument Engineering. 2023;66(6):514-518. (In Russ.) https://doi.org/10.17586/0021-3454-2023-66-6-514-518
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