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Titolo:
Globally optimal bounding ellipsoid algorithm for parameter estimation using artificial neural networks
Autore:
Sun, XF; Fan, YZ; Zhang, FZ;
Indirizzi:
Beijing Univ Aeronaut & Astronaut, Dept Automat Control, Beijing 100083, Peoples R China Beijing Univ Aeronaut & Astronaut Beijing Peoples R China 100083 R China
Titolo Testata:
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
fascicolo: 1, volume: 31, anno: 2000,
pagine: 47 - 53
SICI:
0020-7721(200001)31:1<47:GOBEAF>2.0.ZU;2-F
Fonte:
ISI
Lingua:
ENG
Soggetto:
SET MEMBERSHIP UNCERTAINTY; IDENTIFICATION; SYSTEMS; NOISE;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
16
Recensione:
Indirizzi per estratti:
Indirizzo: Sun, XF Beijing Univ Aeronaut & Astronaut, Dept Automat Control, Beijing 100083, Peoples R China Beijing Univ Aeronaut & Astronaut Beijing Peoples R China 100083
Citazione:
X.F. Sun et al., "Globally optimal bounding ellipsoid algorithm for parameter estimation using artificial neural networks", INT J SYST, 31(1), 2000, pp. 47-53

Abstract

This paper develops a real-time implementation of a globally optimal bounding ellipsoid (GOBE) algorithm for parameter estimation of linear-in-parameter models with unknown but bounded (UBB) errors. A recently proposed recursively optimal bounding ellipsoid (ROBE) algorithm is introduced and a GOBEalgorithm is derived through repeating this ROBE algorithm. An analogue artificial neural network (ANN) is provided to implement the GOBE algorithm in real time. Convergence analyses on the ROBE, the GOBE algorithms, and theanalogue ANN implementation of the GOBE algorithm are presented. No persistent excitation condition is required to ensure the convergence. Simulationresults show the good performances of these algorithms and the ANN implementation.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 24/10/20 alle ore 14:44:30