Library Subscription: Guest
Begell Digital Portal Begell Digital Library eBooks Journals References & Proceedings Research Collections
Journal of Automation and Information Sciences
SJR: 0.232 SNIP: 0.464 CiteScore™: 0.27

ISSN Print: 1064-2315
ISSN Online: 2163-9337

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.v28.i3-4.100
pages 83-100

An Algorithm for Nonstochastic Identification of Controlled-Object Parameters

Z. Zh. Ametzhanova


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.