Worst-case asymptotic properties of linear algorithms for H∞ identification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)5314-5319
Journal / PublicationProceedings of the IEEE Conference on Decision and Control
Volume5
Publication statusPublished - 1999
Externally publishedYes

Conference

TitleThe 38th IEEE Conference on Decision and Control (CDC)
CityPhoenix, AZ, USA
Period7 - 10 December 1999

Link(s)

Check@CityULib
Link to Scopus https://www.scopus.com/record/display.uri?eid=2-s2.0-0033314118&origin=recordpage
Permanent Link https://scholars.cityu.edu.hk/en/publications/publication(ce74d9b3-3347-4c58-a959-3ace2de3da9d).html

Abstract

This paper considers asymptotic properties for linear algorithms in H∞ identification. The divergence of linear algorithms is characterized for H∞ identification in both time and frequency domain. The sample complexity issue is also investigated. The results of this paper complement the existing results for linear algorithms in H∞ identification.

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100
Linear algorithm Mathematics
87
Airway Obstruction Chemical Compounds
37
Asymptotic properti... Mathematics
18
Frequency domain Mathematics
17
Time domain Mathematics
17
Divergence Mathematics
16
Complement Mathematics
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Citation Format(s)

[ RIS ] [ BibTeX ]
Worst-case asymptotic properties of linear algorithms for H∞ identification. / Chen, Jie; Gu, Guoxiang.
In: Proceedings of the IEEE Conference on Decision and Control, Vol. 5, 1999, p. 5314-5319.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 22_Publication in policy or professional journal