Recursive Least-Squares Time Domain Identification of Structural Parameters

1977 ◽  
Vol 44 (1) ◽  
pp. 135-140 ◽  
Author(s):  
P. Caravani ◽  
M. L. Watson ◽  
W. T. Thomson
Keyword(s):  
Least Squares ◽  
Structural Parameters ◽  
Gaussian White Noise ◽  
Initial Guess ◽  
Recursive Least Squares ◽  
Rapid Convergence ◽  
Domain Identification ◽  
Response Data ◽  
Dynamic Excitation ◽  
Two Degree Of Freedom

A method of identifying structural parameters such as damping and stiffness of a building from its time response under dynamic excitation is presented. A least-squares recursive computer algorithm which requires no matrix inversion is developed and tested with the response of a two-degree-of-freedom structure including Gaussian white noise. The algorithm provides means to account for both the model uncertainty and the investigators’ confidence in the initial guess of the parameters. These statistical quantities can be updated with passage of time. The study indicates that rapid convergence to the correct values of the parameters takes place even under severe noise in the response data.

Identification of Structural Parameters in Mistuned Bladed Disks

1997 ◽  
Vol 119 (3) ◽  
pp. 428-438 ◽  
Author(s):  
Marc P. Mignolet ◽  
Chung-Chih Lin
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Structural Model ◽  
Structural Parameters ◽  
Model Parameters ◽  
Disk Model ◽  
Response Data ◽  
State Response ◽  
Bladed Disk ◽  
True Model

The present investigation focused on the estimation of the parameters of a structural model to represent “at best” a set of measurements of the steady state response of a mistuned bladed disk. The applicability of the least squares and maximum likelihood approaches to the identification of the bladed disk model from this data is first investigated. The advantages and drawbacks of these techniques motivate the introduction of a new mixed least squares-maximum likelihood formulation which is shown to recover well the true model parameters from noisy simulated response data.

Quantitative analysis by reconstruction of HREM focal series

1994 ◽  
Vol 52 ◽  
pp. 740-741
Author(s):  
W. Coene ◽  
A. Thust ◽  
M. Op de Beeck ◽  
D. Van Dyck
Keyword(s):  
Phase Retrieval ◽  
Image Plane ◽  
Initial Guess ◽  
Recursive Least Squares ◽  
Objective Lens ◽  
Electron Wave ◽  
Electron Sources ◽  
Original Algorithm ◽  
Coherent Field ◽  
Direct Interpretation

Compared to conventional electron sources, the use of a highly coherent field-emission gun (FEG) in TEM improves the information resolution considerably. A direct interpretation of this extra information, however, is hampered since amplitude and phase of the electron wave are scrambled in a complicated way upon transfer from the specimen exit plane through the objective lens towards the image plane. In order to make the additional high-resolution information interpretable, a phase retrieval procedure is applied, which yields the aberration-corrected electron wave from a focal series of HRTEM images (Coene et al, 1992).Kirkland (1984) tackled non-linear image reconstruction using a recursive least-squares formalism in which the electron wave is modified stepwise towards the solution which optimally matches the contrast features in the experimental through-focus series. The original algorithm suffers from two major drawbacks : first, the result depends strongly on the quality of the initial guess of the first step, second, the processing time is impractically high.

scholarly journals Identification by Recursive Least Squares with Kalman Filter (RLS-KF) Applied to a Robotic Manipulator

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Darielson A. Souza ◽  
Josias G. Batista ◽  
Felipe J. S. Vasconcelos ◽  
Laurinda L. N. Dos Reis ◽  
Gabriel F. Machado ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Robotic Manipulator ◽  
Recursive Least Squares
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