logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Model updating using robust estimation

delete2002-01-01
delete50
PRE
AI
C
Cristinel Mares
M
Michael I. Friswell
J
John E. Mottershead
DOI：10.1006/mssp.2000.1375delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Model updating attempts to correct errors in a finite element model using measured data. However, the measurements are corrupted with noise and the finite element model contains errors. Most updating schemes try to find the best model in some mean sense. This paper takes a different view and uses robust estimation techniques to determine the most robust parameter set. This parameter set is optimum in that the residuals are as small as can be, for a range of bounded uncertainties on the model and measurements. The relationship between this robust identification and Tikhonov regularisation is explored. The use of the 'L' curve method and expected parameter uncertainty to determine the regularisation parameter are shown. The approach is demonstrated using a simulated cantilever beam example, and experimental data from the GARTEUR test structure. (C) 2002 Elsevier Science Ltd.
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Mechanical Systems and Signal Processing cover
Mechanical Systems and Signal Processing
IF:
8.9
Papers:
1.3W
Citations:
6.6W

Organization

No organization information available