On the use of GP-NARX model for damage detection in intrinsically nonlinear systems-Reference-Cited by-同舟云学术

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On the use of GP-NARX model for damage detection in intrinsically nonlinear systems

Published:2024-01-23 Issue:2 Volume:46 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Mazzoni André Vila Nova,Villani Luis Gustavo Giacon^ORCID

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40430-023-04657-1.pdf

Reference33 articles.

1. (2020) On the application of gaussian process latent force models for joint input-state-parameter estimation: with a view to Bayesian operational identification. Mech Syst Signal Process 140:106580. https://doi.org/10.1016/j.ymssp.2019.106580

2. (2022) A latent restoring force approach to nonlinear system identification. Mechanical Systems and Signal Processing 180:109426. https://doi.org/10.1016/j.ymssp.2022.109426

3. Antoniadis A, Lambert-Lacroix S, Poggi JM (2021) Random forests for global sensitivity analysis: a selective review. Reliab Eng Syst Saf 206:107312. https://doi.org/10.1016/j.ress.2020.107312

4. Billings SA (2013) Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley, New Jersey

5. Bishop CM, Nasrabadi NM (2006) Pattern recognition and machine learning, vol 4. Springer, New York

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1. Data-driven Dirichlet sampling on manifolds for structural health monitoring;Journal of the Brazilian Society of Mechanical Sciences and Engineering;2024-06-03

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