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Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Imprimir: 1064-2315
ISSN On-line: 2163-9337

Volumes:
Volume 51, 2019 Volume 50, 2018 Volume 49, 2017 Volume 48, 2016 Volume 47, 2015 Volume 46, 2014 Volume 45, 2013 Volume 44, 2012 Volume 43, 2011 Volume 42, 2010 Volume 41, 2009 Volume 40, 2008 Volume 39, 2007 Volume 38, 2006 Volume 37, 2005 Volume 36, 2004 Volume 35, 2003 Volume 34, 2002 Volume 33, 2001 Volume 32, 2000 Volume 31, 1999 Volume 30, 1998 Volume 29, 1997 Volume 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v46.i3.20
pages 3-16

Systems Identification and Model Order Reduction by Direct Variational Method

Vyacheslav F. Gubarev
Institute of Space Research of National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Kiev, Ukraine
Olena V. Vizniuk
Institute for Applied Systems Analysis of National Technical University of Ukraine "Kiev Polytechnic Institute" of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kiev
Dmitriy M. Parkhomchuk
Institute for Applied Systems Analysis of National Technical University of Ukraine "Kiev Polytechnic Institute" of National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, Kiev

RESUMO

The processes of identification and a model order reduction on the basis of a direct variational method are investigated. In the case of deterministic data it was shown that the results of identification do not depend on the amplitude of the input signal, the minimal informative length of the observation interval was found. In the case of approximate data there were investigated stability properties of different models.


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