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Proceedings Paper

Bayesian parameter estimation of Euler-Bernoulli beams
Author(s): Iman T. Ardekani; Jari Kaipio; Neda Sakhaee; Hamid Sharifzadeh
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Paper Abstract

This paper develops a statistical signal processing algorithm for parameter estimation of Euler-Bernoulli beams from limited and noisy measurement. The original problem is split into two reduced-order sub-problems coupled by a linear equation. The first sub-problem is cast as an inverse problem and solved by using Bayesian approximation error analysis. The second sub-problem is cast as a forward problem and solved by using the finite element technique. An optimal solution to the original problem is then obtained by coupling the solutions to the two sub-problems. Finally, a statistical signal processing algorithm for adaptive estimation of the optimal solution is developed. Computer simulation shows the effectiveness of the proposed algorithm.

Paper Details

Date Published: 17 April 2019
PDF: 6 pages
Proc. SPIE 11071, Tenth International Conference on Signal Processing Systems, 110710A (17 April 2019); doi: 10.1117/12.2520452
Show Author Affiliations
Iman T. Ardekani, Unitec Institute of Technology (New Zealand)
Jari Kaipio, The Univ. of Auckland (New Zealand)
Neda Sakhaee, The Univ. of Auckland (New Zealand)
Hamid Sharifzadeh, Unitec Institute of Technology (New Zealand)


Published in SPIE Proceedings Vol. 11071:
Tenth International Conference on Signal Processing Systems
Kezhi Mao; Xudong Jiang, Editor(s)

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