Identification of Non-Stationary Parameters of a Linear Regression Model Under Additive Influence of the Unmeasurable Sinusoidal Disturbance
https://doi.org/10.17586/0021-3454-2022-65-7-492-499
Abstract
In the framework of the deterministic approach, an algorithm for identifying nonstationary parameters for the classical linear regression equation is proposed. When synthesizing the algorithm for estimating non-stationary parameters, it is assumed that the dynamic model of their variation is known and is a linear generator with variable coefficients. An additional complication of the problem of estimating parameters for the linear regression equation is the presence of an additive sinusoidal disturbing effect with unknown constant amplitudes, frequencies, and phases. The resulting algorithm provides an accurate estimation of all unknown non-stationary parameters.
Keywords
About the Authors
A. A. BobtsovRussian Federation
Alexey A. Bobtsov – Dr. Sci., Professor; Faculty of Control Systems and Robotics
St. Petersburg
A. V. Kaplin
Russian Federation
Alexey V. Kaplin – Post-Graduate Student; Faculty of Control Systems and Robotics
St. Petersburg
N. A. Nikolaev
Russian Federation
Nikolay A. Nikolaev – PhD, Associate Professor; Faculty of Control Systems and Robotics
St. Petersburg
O. V. Oskina
Russian Federation
Olga V. Oskina – Post-Graduate Student; Faculty of Control Systems and Robotics
St. Petersburg
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Review
For citations:
Bobtsov A.A., Kaplin A.V., Nikolaev N.A., Oskina O.V. Identification of Non-Stationary Parameters of a Linear Regression Model Under Additive Influence of the Unmeasurable Sinusoidal Disturbance. Journal of Instrument Engineering. 2022;65(7):492-499. https://doi.org/10.17586/0021-3454-2022-65-7-492-499
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