Abstract
The aim of this paper is to investigate and illustrate the possibilities of using correlation functions estimated by the Random Decrement Technique as a basis for parameter identification. A two-stage system identification system is used: first, the correlation functions are estimated by the Random Decrement Technique, and then the system parameters are identified from the correlation function estimates. Three different techniques are used in the parameter identification process: a simple non-parametric method, estimation of an Auto Regressive (AR) model by solving an overdetermined set of Yule-Walker equations and finally, least-square fitting of the theoretical correlation function. The results are compared to the results of fitting an Auto Regressive Moving Average (ARMA) model directly to the system output from a single-degree-of-freedom system loaded by white noise.
Original language | English |
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Title of host publication | Proceedings of the Florence Modal Analysis Conference |
Number of pages | 8 |
Place of Publication | Firenze |
Publisher | Fintito di Stampare presso il Centro Duplicazione Offset |
Publication date | Sept 1991 |
Pages | 465-472 |
Publication status | Published - Sept 1991 |
Event | Florence Modal Analysis Conference - Firenze, Italy Duration: 10 Sept 1991 → 12 Sept 1991 |
Conference
Conference | Florence Modal Analysis Conference |
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Country/Territory | Italy |
City | Firenze |
Period | 10/09/1991 → 12/09/1991 |
Keywords
- RDD
- Parameter Identifications
- AR
- ARMA