doi: 10.17586/2226-1494-2016-16-3-565-567


S. V. Aranovskiy, A. A. Bobtsov, J. Wang, N. A. Nikolaev, A. A. Pyrkin

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Article in Russian

For citation: Aranovskiy S.V., Bobtsov A.A., Wang J., Nikolaev N.A., Pyrkin A.A. Identification properties enhancement algorithm for problems of parameters estimation of linear regression model. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 3, pp. 565–567. doi: 10.17586/2226-1494-2016-16-3-565-567


This brief paper describes a new approach to identification of unknown constant parameters for a linear regression model. The main idea of the method lies in transformation of initial model into a new kind one. The new model regressor possesses identification properties or meets persistency of excitation conditions. An example of two unknown parameters identification for the linear regression model shows efficiency of the proposed approach. Simulation was carried out for a regressor with no persistency of excitation conditions, hence, parameter identification is not guaranteed.

Keywords: parameters identification, persistency of excitation conditions

Acknowledgements. This work was partially financially supported by the Government of the Russian Federation, (Grant 074-U01) and by the Ministry of Education and Science of the Russian Federation (Project 14.Z50.31.0031).


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