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    Bayesian parameter estimation of Euler-Bernoulli beams

    Ardekani, Iman; Kaipio, J.; Sharifzadeh, Hamid

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    Ardekani, I. (2018).pdf (296.1Kb)
    Date
    2018-11
    Citation:
    Ardekani, I., Kaipio, J., & Sharifzadeh, H. (2018). Bayesian parameter estimation of Euler-Bernoulli beams.10th International Conference on Signal Processing Systems (ICSPS 2018) Retrieved from http://www.icsps.org/
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/4438
    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:
    statistical signal processing, Euler-Bernoulli beams, Bayesian approximation error, system identification
    ANZSRC Field of Research:
    091301 Acoustics and Noise Control (excl. Architectural Acoustics)
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Conference Papers [147]

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