eprintid: 2450
rev_number: 6
eprint_status: archive
userid: 6
dir: disk0/00/00/24/50
datestamp: 2015-01-09 12:25:17
lastmod: 2015-01-09 12:25:17
status_changed: 2015-01-09 12:25:17
type: book_section
metadata_visibility: show
creators_name: Cerone, Vito
creators_name: Piga, Dario
creators_name: Regruto, Diego
creators_id: 
creators_id: dario.piga@imtlucca.it
creators_id: 
title: FIR approximation of linear systems from quantized records
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: Bounded Error Identification
note: 16th IFAC Symposium on System Identification held in Brussels (Belgium), 11-13 July 2012
abstract: In this paper we consider the problem of identifying a fixed-order FIR approximation of linear systems with unknown structure, assuming that both input and output measurements are subjected to quantization. In particular, a FIR model of given order which provides the best approximation of the input-output relationship is sought by minimizing the worst-case distance between the output of the true system and the modeled output, for all possible values of the input and output data consistent with their quantized measurements. First we show that the considered problem can be formulated in terms of robust optimization. Then, we present two different algorithms to compute the optimum of the formulated problem by means of linear programming techniques. The effectiveness of the proposed approach is illustrated by means of a simulation example.
date: 2012
publisher: IFAC
pagerange: 1179-1184
id_number: doi:10.3182/20120711-3-BE-2027.00239
refereed: TRUE
isbn: 978-3-902823-06-9
issn: 1474-6670
book_title: 16th IFAC Symposium on System Identification
official_url: http://dx.doi.org/10.3182/20120711-3-BE-2027.00239
citation:   Cerone, Vito and Piga, Dario and Regruto, Diego  FIR approximation of linear systems from quantized records.   In:  16th IFAC Symposium on System Identification.   IFAC, pp. 1179-1184.  ISBN 978-3-902823-06-9      (2012)