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Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

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

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v28.i3-4.100
pages 83-100

An Algorithm for Nonstochastic Identification of Controlled-Object Parameters

Z. Zh. Ametzhanova

Краткое описание

The set-theoretic (nonstochastic, guaranteed) approach [1-3, 5-7] is used widely in for solving identification problems when only the sets of the possible values of the object's unknown parameters are specified. This approach allows one to track the evolution of the multidimensional ellipsoidal estimates (approximations) that are guaranteed to contain the object's unknown parameters vector. According to the general schema for solving identification problems by the set-theoretic approach, every ellipsoidal estimate is an intersection of two sets. One of them is the result of processing all past information, and the other corresponds to the latest measurement of the object output. Investigations associated with the construction of set estimates of the unknown parameters vector and the solution of the identification problem in its nonstatic posing are continued in this paper. In contrast with existing algorithms, a new structural variant of the ellipsoidal estimates of the unknown parameters for the case in which several layers that correspond to the latest measurements of the object output intersect with the a-priori ellipsoidal set is proposed here.