IDENTIFICATION PROPERTIES ENHANCEMENT ALGORITHM FOR PROBLEMS OF PARAMETERS ESTIMATION OF LINEAR REGRESSION MODEL

Autor:	S. V. Aranovskiy, A. A. Bobtsov, J. . Wang, N. A. Nikolaev, A. A. Pyrkin
Jazyk:	English<br />Russian
Rok vydání:	2016
Předmět:	parameters identification persistency of excitation conditions Optics. Light QC350-467 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Naučno-tehničeskij Vestnik Informacionnyh Tehnologij, Mehaniki i Optiki, Vol 16, Iss 3, Pp 565-567 (2016)
Druh dokumentu:	article
ISSN:	2226-1494 2500-0373
DOI:	10.17586/2226-1494-2016-16-3-565-567
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/a4bf94be3eef437f85e264dc5dd6d51f Zobrazit plný text záznamu View record in DOAJ