A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropri...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
SAGE Publishing
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3303672e0083406dbfe4594f6f84bbca |
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