Implementation of some Bayesian Filters for structural system identification
gráficos, tablas
- Autores:
-
Jaramillo Moreno, Sebastian
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2018
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82084
- Palabra clave:
- 620 - Ingeniería y operaciones afines
Sistemas Estructurales
Structural Systems
Bayesian filters
Bayesian inference
Kalman filter
Unscented Kalman filter
Particle filter
Filtros bayesianos
Inferencia bayesiana
Filtro de Kalman
Filtro de Kalman Unscented
Filtro de Partículas
- Rights
- openAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional
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| dc.title.eng.fl_str_mv |
Implementation of some Bayesian Filters for structural system identification |
| dc.title.translated.spa.fl_str_mv |
Implementación de algunos Filtros Bayesianos para la identificación de sistemas estructurales |
| title |
Implementation of some Bayesian Filters for structural system identification |
| spellingShingle |
Implementation of some Bayesian Filters for structural system identification 620 - Ingeniería y operaciones afines Sistemas Estructurales Structural Systems Bayesian filters Bayesian inference Kalman filter Unscented Kalman filter Particle filter Filtros bayesianos Inferencia bayesiana Filtro de Kalman Filtro de Kalman Unscented Filtro de Partículas |
| title_short |
Implementation of some Bayesian Filters for structural system identification |
| title_full |
Implementation of some Bayesian Filters for structural system identification |
| title_fullStr |
Implementation of some Bayesian Filters for structural system identification |
| title_full_unstemmed |
Implementation of some Bayesian Filters for structural system identification |
| title_sort |
Implementation of some Bayesian Filters for structural system identification |
| dc.creator.fl_str_mv |
Jaramillo Moreno, Sebastian |
| dc.contributor.advisor.none.fl_str_mv |
Alvarez Marín, Diego Andrés |
| dc.contributor.author.none.fl_str_mv |
Jaramillo Moreno, Sebastian |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines |
| topic |
620 - Ingeniería y operaciones afines Sistemas Estructurales Structural Systems Bayesian filters Bayesian inference Kalman filter Unscented Kalman filter Particle filter Filtros bayesianos Inferencia bayesiana Filtro de Kalman Filtro de Kalman Unscented Filtro de Partículas |
| dc.subject.lemb.spa.fl_str_mv |
Sistemas Estructurales |
| dc.subject.lemb.eng.fl_str_mv |
Structural Systems |
| dc.subject.proposal.eng.fl_str_mv |
Bayesian filters Bayesian inference Kalman filter Unscented Kalman filter Particle filter |
| dc.subject.proposal.spa.fl_str_mv |
Filtros bayesianos Inferencia bayesiana Filtro de Kalman Filtro de Kalman Unscented Filtro de Partículas |
| description |
gráficos, tablas |
| publishDate |
2018 |
| dc.date.issued.none.fl_str_mv |
2018-12 |
| dc.date.accessioned.none.fl_str_mv |
2022-08-24T21:38:00Z |
| dc.date.available.none.fl_str_mv |
2022-08-24T21:38:00Z |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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Image Text |
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http://purl.org/coar/resource_type/c_7a1f |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/82084 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/82084 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[Ahsan and O’Connor, 1994] Ahsan, M. and O’Connor, K. M. (1994). A reappraisal of the kalman filtering technique, as applied in river flow forecasting. Journal of Hydrology, 161(1-4):197–226. [Arulampalam et al., 2002] Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on signal processing, 50(2):174–188. [Bertsekas and Tsitsiklis, 2002] Bertsekas, D. P. and Tsitsiklis, J. N. (2002). Introduction to probability, volume 1. Athena Scientific Belmont, MA. [Bouc, 1967] Bouc, R. (1967). Forced vibrations of mechanical systems with hysteresis. In Proc. of the Fourth Conference on Nonlinear Oscillations, Prague. [Chen, 1998] Chen, C.-T. (1998). Linear system theory and design. Oxford University Press, Inc. [Clough and Penzien, 1995] Clough, R. W. and Penzien, J. (1995). Dynamics of Structures. Berkeley: Computers & Structures, Inc. [Doucet et al., 2001] Doucet, A., De Freitas, N., and Gordon, N. (2001). An introduction to sequential monte carlo methods. Sequential Monte Carlo Methods in Practice, pages 3–13. [Doucet et al., 2000] Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and computing. [Farrar and Worden, 2012] Farrar, C. R. and Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley & Sons. [Majidi Khalilabad et al., 2018] Majidi Khalilabad, N., Mollazadeh, M., Akbarpour, A., and Khorashadizadeh, S. a. (2018). Leak detection in water distribution system using non-linear kalman filter. International Journal of Optimization in Civil Engineering. [Mendenhall et al., 2012] Mendenhall, W., Beaver, R. J., and Beaver, B. M. (2012). Introduction to probability and statistics. Cengage Learning. [Papoulis and Pillai, 2002] Papoulis, A. and Pillai, S. U. (2002). Probability, random variables, and stochastic processes. Tata McGraw-Hill Education. [Särkkä, 2013] Särkkä, S. (2013). Bayesian Filtering and Smoothing. Bayesian Filtering and Smoothing. Cambridge University Press. [Wan and Van Der Merwe, 2001] Wan, E. A. and Van Der Merwe, R. (2001). The unscented kalman filter. Kalman filtering and neural networks. [Wikipedia, 2018] Wikipedia (2018). Bouc–wen model of hysteresis. https:// en.wikipedia.org/wiki/Bouc-Wen_model_of_hysteresis. [Online; accessed 7- November-2018]. [Wu and Smyth, 2007] Wu, M. and Smyth, A. W. (2007). Application of the unscented kalman filter for real-time nonlinear structural system identification. Structural Control and Health Monitoring. |
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http://purl.org/coar/access_right/c_abf2 |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional |
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http://creativecommons.org/licenses/by-nc-sa/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional http://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
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openAccess |
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xviii, 90 páginas |
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application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Ingeniería Civil |
| dc.publisher.department.spa.fl_str_mv |
Departamento de Ingeniería Civil |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería y Arquitectura |
| dc.publisher.place.spa.fl_str_mv |
Manizales, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Manizales |
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Universidad Nacional de Colombia |
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Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Alvarez Marín, Diego Andrésd383c3aef5a53c7df5fa6f1fa9be0f0fJaramillo Moreno, Sebastian3e8b63c9c01b61f17e7ede98052102c92022-08-24T21:38:00Z2022-08-24T21:38:00Z2018-12https://repositorio.unal.edu.co/handle/unal/82084Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/gráficos, tablasThe present study deals with three different methods for structural identification: the Kalman filter, the Unscented Kalman filter and the Particle filter. The Kalman filter is a known filter for state estimation in linear systems. To perform the estimation in non-linear systems, methods such as the Unscented Kalman filter and the Particle filter were developed. The Unscented Kalman filter uses the Unscented transform to approximate the different distributions to a Gaussian, allowing it to have certain similarities with the Kalman filter. The Particle filter uses Monte Carlo methods to generate samples of arbitrary probability distributions in various dimensions, which are propagated through the system in order to approximate values of the new probability distribution. Finally, a set of examples are made that allow to compare the accuracy and computational speed of the different filters and evaluate their performance. (Texto tomado de la fuente)El presente estudio trata tres diferentes métodos para identificación estructural: el Filtro de Kalman, el Filtro de Kalman Unscented y el Filtro de Partículas. El Filtro de Kalman es un conocido Filtro para la estimación de estados en sistemas lineales. Para realizar la estimación en sistemas no-lineales, se desarrollaron métodos como el Filtro de Kalman Unscented y el Filtro de Partículas. El Filtro de Kalman Unscented usa la transformada Unscented para aproximar las diferentes distribuciones a Gaussianas, permitiéndole tener ciertas similitudes con el Filtro de Kalman. El Filtro de Partículas usa métodos de Monte Carlo para generar muestras de distribuciones de probabilidad arbitrarias en varias dimensiones, las cuales se propagan por el sistema para conocer una forma aproximada de la nueva distribución de probabilidad. Finalmente, se realiza una serie de ejemplos que permiten comparar la precisión y la velocidad computacional de los diferentes filtros y evaluar su desempeño.Ganador de la Convocatoria: “Mejores Trabajos de Grado de Pregrado” Versión XXVIII - Resolución 010 de 2019PregradoIngeniero CivilIngeniería Civilxviii, 90 páginasapplication/pdfengUniversidad Nacional de ColombiaManizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Ingeniería CivilDepartamento de Ingeniería CivilFacultad de Ingeniería y ArquitecturaManizales, ColombiaUniversidad Nacional de Colombia - Sede Manizales620 - Ingeniería y operaciones afinesSistemas EstructuralesStructural SystemsBayesian filtersBayesian inferenceKalman filterUnscented Kalman filterParticle filterFiltros bayesianosInferencia bayesianaFiltro de KalmanFiltro de Kalman UnscentedFiltro de PartículasImplementation of some Bayesian Filters for structural system identificationImplementación de algunos Filtros Bayesianos para la identificación de sistemas estructuralesTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fImageText[Ahsan and O’Connor, 1994] Ahsan, M. and O’Connor, K. M. (1994). A reappraisal of the kalman filtering technique, as applied in river flow forecasting. Journal of Hydrology, 161(1-4):197–226.[Arulampalam et al., 2002] Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on signal processing, 50(2):174–188.[Bertsekas and Tsitsiklis, 2002] Bertsekas, D. P. and Tsitsiklis, J. N. (2002). Introduction to probability, volume 1. Athena Scientific Belmont, MA.[Bouc, 1967] Bouc, R. (1967). Forced vibrations of mechanical systems with hysteresis. In Proc. of the Fourth Conference on Nonlinear Oscillations, Prague.[Chen, 1998] Chen, C.-T. (1998). Linear system theory and design. Oxford University Press, Inc.[Clough and Penzien, 1995] Clough, R. W. and Penzien, J. (1995). Dynamics of Structures. Berkeley: Computers & Structures, Inc.[Doucet et al., 2001] Doucet, A., De Freitas, N., and Gordon, N. (2001). An introduction to sequential monte carlo methods. Sequential Monte Carlo Methods in Practice, pages 3–13.[Doucet et al., 2000] Doucet, A., Godsill, S., and Andrieu, C. (2000). On sequential monte carlo sampling methods for bayesian filtering. Statistics and computing.[Farrar and Worden, 2012] Farrar, C. R. and Worden, K. (2012). Structural health monitoring: a machine learning perspective. John Wiley & Sons.[Majidi Khalilabad et al., 2018] Majidi Khalilabad, N., Mollazadeh, M., Akbarpour, A., and Khorashadizadeh, S. a. (2018). Leak detection in water distribution system using non-linear kalman filter. International Journal of Optimization in Civil Engineering.[Mendenhall et al., 2012] Mendenhall, W., Beaver, R. J., and Beaver, B. M. (2012). Introduction to probability and statistics. Cengage Learning.[Papoulis and Pillai, 2002] Papoulis, A. and Pillai, S. U. (2002). Probability, random variables, and stochastic processes. Tata McGraw-Hill Education.[Särkkä, 2013] Särkkä, S. (2013). Bayesian Filtering and Smoothing. Bayesian Filtering and Smoothing. Cambridge University Press.[Wan and Van Der Merwe, 2001] Wan, E. A. and Van Der Merwe, R. (2001). The unscented kalman filter. Kalman filtering and neural networks.[Wikipedia, 2018] Wikipedia (2018). Bouc–wen model of hysteresis. https:// en.wikipedia.org/wiki/Bouc-Wen_model_of_hysteresis. [Online; accessed 7- November-2018].[Wu and Smyth, 2007] Wu, M. and Smyth, A. W. (2007). Application of the unscented kalman filter for real-time nonlinear structural system identification. Structural Control and Health Monitoring.BibliotecariosEstudiantesInvestigadoresMaestrosPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.unal.edu.co/bitstream/unal/82084/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51ORIGINAL114026.2018.pdf114026.2018.pdfTesis de Ingeniería Civilapplication/pdf10478064https://repositorio.unal.edu.co/bitstream/unal/82084/2/114026.2018.pdfcff2a612f0feb6bafff7f19a0a555170MD52THUMBNAIL114026.2018.pdf.jpg114026.2018.pdf.jpgGenerated Thumbnailimage/jpeg3861https://repositorio.unal.edu.co/bitstream/unal/82084/3/114026.2018.pdf.jpg02e52d01c8074165c7e0973644353653MD53unal/82084oai:repositorio.unal.edu.co:unal/820842024-08-09 23:20:44.766Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |