Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
Bounded error parameter estimation: A sequential analytic center approach
Autore:
Bai, EW; Ye, YY; Tempo, R;
Indirizzi:
Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA Univ Iowa IowaCity IA USA 52242 ect & Comp Engn, Iowa City, IA 52242 USA Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA Univ Iowa Iowa City IA USA 52242 Management Sci, Iowa City, IA 52242 USA Politecn Torino, CNR, CENS, Turin, Italy Politecn Torino Turin ItalyPolitecn Torino, CNR, CENS, Turin, Italy
Titolo Testata:
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
fascicolo: 6, volume: 44, anno: 1999,
pagine: 1107 - 1117
SICI:
0018-9286(199906)44:6<1107:BEPEAS>2.0.ZU;2-V
Fonte:
ISI
Lingua:
ENG
Soggetto:
SET MEMBERSHIP UNCERTAINTY; LEAST-SQUARES; IDENTIFICATION; ALGORITHMS; NOISE; COMPLEXITY; SYSTEMS; MODELS;
Keywords:
bounded error estimation; membership methods; parameter estimation; system identification;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
25
Recensione:
Indirizzi per estratti:
Indirizzo: Bai, EW Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA Univ Iowa Iowa City IA USA 52242 mp Engn, Iowa City, IA 52242 USA
Citazione:
E.W. Bai et al., "Bounded error parameter estimation: A sequential analytic center approach", IEEE AUTO C, 44(6), 1999, pp. 1107-1117

Abstract

In this paper, a sequential analytic center approach for bounded error parameter estimation is proposed. The authors show that the analytic center minimizes the logarithmic average output error among all the estimates withinthe membership set and is a maximum likelihood estimator for a class of noise density functions which include parabolic densities and approximations of truncated Gaussian. They also show that the analytic center is easily computable for both offline and online problems with a sequential algorithm. The convergence proof of this sequential algorithm is obtained and, moreover, it is shown that the complexity in terms of the maximum number of Newtoniterations is linear in the number of observed data points.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 29/11/20 alle ore 16:30:34