Email this article Recursive parametrical identification of multidimensional linear dynamic systems with local autocorrelated noises in input and output signals
- Authors: Ivanov D.V1, Katsyuba O.A1
-
Affiliations:
- Samara State Transport University
- Issue: Vol 15, No 4 (2011)
- Pages: 102-109
- Section: Articles
- URL: https://journals.eco-vector.com/1991-8615/article/view/20954
- ID: 20954
Cite item
Full Text
Abstract
The recursive algorithm allowing to receive strongly consistent estimates of parameters of multidimensional on an input linear dynamic systems with locally autocorrelated noise in input and output signals is suggested. Numerical examples are included to illustrate the effectiveness of the proposed algorithm.
About the authors
Dmitriy V Ivanov
Samara State Transport University
Email: dvi85@list.ru
(к.ф.-м.н.), ст. преподаватель, каф. мехатроники в автоматизированных производствах; Самарский государственный университет путей сообщения; Samara State Transport University
Oleg A Katsyuba
Samara State Transport University
Email: katsuba.samgups@mail.ru
(д.т.н., проф.), зав. кафедрой, каф. мехатроники в автоматизированных производствах; Самарский государственный университет путей сообщения; Samara State Transport University
References
- Söderström T., Mahata K. On instrumental variable and total least squares approaches for identification of noisy systems // Int. J. Control, 2002. Vol. 75, no. 6. Pp. 381-389.
- Thil S., Söderström T., Garnier H. On instrumental variable-based methods for errors-inovariables model identification / In: Proc. 17th IFAC World Congress, Seoul, Korea / World Congress, 17 (Part 1), 2008. Pp. 426-431.
- Ikenoue M., Kanae Z.-J., Wada K. Identification of noisy input-output system using biascompensated least squares method / In: Proc. 16th IFAC World Congress on Automatic Control, Prague, Czech Republic / World Congress, 16 (Part 1), 2005. Pp. 803-808.
- Ekman M., Hong M., Wada K., Söderström T. A separable nonlinear least-squares approach for identification of linear systems with errors in variables / In: Proc. 14th IFAC Symposium on System Identification, Newcastle, Australia / World Congress, 14 (Part 1), 2006. Pp. 173-183.
- Кацюба О. А. Теория идентификации стохастических динамических систем в условиях неопределённости. Самара: СамГУПС, 2008. 119 с.
- Деревицкий Д. П., Фрадков А. Л. Прикладная теория дискретных адаптивных систем управления. M.: Наука, 1991. 215 с.
- Chen H. F. Stochastic Approximation and Its Applications. Dordrecht: Kluwer, 2002. 357 pp.
- Chen H. F. A Unified Approach to Recursive System Identification / In: Proc. 17th IFAC Symposium on System Identification, Saint-Malo, France / World Congress, 17 (Part 1), 2009. Pp. 420-425.
- Беллман Р. Введение в теорию матриц. М.: Наука, 1989. 376 с.
Supplementary files
