DOI 10.17586/0021-3454-2023-66-6-514-518
UDC 681.5.015
SIMPLIFIED IDENTIFICATION ALGORITHM FOR CLASSICAL LINEAR REGRESSION CONTAINING POWER FUNCTIONS OF UNKNOWN PARAMETER
ITMO University, Saint Petersburg, 197101, Russian Federation; Postgraduate, Research Engineer
A. A. Bobtsov
ITMO University, Saint Petersburg, 197101, Russian Federation; Head of the School of Computer Technologies and Control, Professor at the Faculty of Control Systems and Robotics, Head of the Adaptive and Nonlinear Control Systems Lab
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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.
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