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This article is cited in 10 scientific papers (total in 10 papers)
Stochastic Systems
Identification of piecewise linear parameters of regression models of non-stationary deterministic systems
Jian Wanga, Tuan Le Vangb, A. A. Pyrkinb, S. A. Kolyubinb, A. A. Bobtsovb a Hangzhou Dianzi University, Hangzhou, China
b ITMO University (National Research University of Information Technologies, Mechanics and Optics), St. Petersburg, Russia
Abstract:
We consider the problem of identifying unknown nonstationary piecewise linear parameters for a linear regression model. A new algorithm is proposed that allows, in the case of a number of assumptions on the elements of the regressor, to provide an estimate of unknown non-stationary parameters. We analyze in detail the case with two unknown parameters, which makes it possible to understand the main idea of the proposed approach. We also consider a generalization to the case of an arbitrary number of parameters. We give an example of computer simulation that illustrates the efficiency of the proposed approach.
Keywords:
identification, sensorless control, biomechatronic systems, linear regression model, dynamic regressor expansion.
Citation:
Jian Wang, Tuan Le Vang, A. A. Pyrkin, S. A. Kolyubin, A. A. Bobtsov, “Identification of piecewise linear parameters of regression models of non-stationary deterministic systems”, Avtomat. i Telemekh., 2018, no. 12, 71–82; Autom. Remote Control, 79:12 (2018), 2159–2168
Linking options:
https://www.mathnet.ru/eng/at15224 https://www.mathnet.ru/eng/at/y2018/i12/p71
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Abstract page: | 264 | Full-text PDF : | 57 | References: | 33 | First page: | 17 |
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