DocumentCode
2098109
Title
Suboptimal algorithms for worst case identification and model validation
Author
Gu, Guoxiang
Author_Institution
Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA, USA
fYear
1993
fDate
15-17 Dec 1993
Firstpage
539
Abstract
New algorithms based on convex programming are proposed for worst case system identification. The algorithms are optimal within a factor of two asymptotically. Further, model validation, or data consistency is embedded in the identification process. Explicit worst case identification error bounds in H∞ norm are also derived for both uniformly and nonuniformly spaced frequency response samples
Keywords
approximation theory; computational complexity; convex programming; frequency response; identification; approximation theory; computational complexity; convex programming; data consistency; error bounds; frequency response samples; model validation; suboptimal algorithms; system identification; worst case identification; Computer aided software engineering; Costs; Error correction; Frequency response; H infinity control; Interpolation; Jacobian matrices; Noise level; System identification; Transfer functions;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location
San Antonio, TX
Print_ISBN
0-7803-1298-8
Type
conf
DOI
10.1109/CDC.1993.325085
Filename
325085
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