@inproceedings{4382e041e017403a968edf4f4c163c6c,
title = "Bayesian parameter estimation of Euler-Bernoulli beams",
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.",
keywords = "system identi cation, Bayesian approximation error, statistical signal processing, Euler-Bernoulli beams",
author = "Ardekani, {Iman T.} and Jari Kaipio and Neda Sakhaee and Hamid Sharifzadeh",
year = "2019",
doi = "10.1117/12.2520452",
language = "English",
isbn = "9781510628359",
series = "Proceedings of SPIE",
publisher = "SPIE",
pages = "110710A--1--110710A--6",
editor = "Kezhi Mao and Xudong Jiang",
booktitle = "Tenth International Conference on Signal Processing Systems",
address = "United States",
note = "10th International Conference on Signal Processing Systems, ICSPS 2018 ; Conference date: 16-11-2018 Through 18-11-2018",
}