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dc.contributor.authorArdekani, Iman
dc.contributor.authorKaipio, J.
dc.contributor.authorSharifzadeh, Hamid
dc.description.abstractThis 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.en_NZ
dc.rightsAll rights reserveden_NZ
dc.subjectstatistical signal processingen_NZ
dc.subjectEuler-Bernoulli beamsen_NZ
dc.subjectBayesian approximation erroren_NZ
dc.subjectsystem identificationen_NZ
dc.titleBayesian parameter estimation of Euler-Bernoulli beamsen_NZ
dc.typeConference Contribution - Paper in Published Proceedingsen_NZ
dc.subject.marsden091301 Acoustics and Noise Control (excl. Architectural Acoustics)en_NZ
dc.identifier.bibliographicCitationArdekani, I., Kaipio, J., & Sharifzadeh, H. (2018). Bayesian parameter estimation of Euler-Bernoulli beams.10th International Conference on Signal Processing Systems (ICSPS 2018) Retrieved from
unitec.conference.title10th International Conference on Signal Processing Systems (ICSPS 2018)en_NZ
unitec.conference.orgInternational Association of Computer Science and Information Technology (IACSIT)en_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
dc.contributor.affiliationUniversity of Aucklanden_NZ

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