Detail publikace

Application of the particle filters for identification of the non-Gaussian systems

LEBEDA, A.

Originální název

Application of the particle filters for identification of the non-Gaussian systems

Anglický název

Application of the particle filters for identification of the non-Gaussian systems

Jazyk

en

Originální abstrakt

This paper focuses on application of a particle filter for online identification of non-Gaussian systems. Firstly, the Bayesian inference was described and then the particle filter was defined. The particle filter numerically solves a problem of a recursive Bayesian state estimator. Secondly, the parameters of the linear system and two types of the non-Gaussian systems were estimated by application of the particle filter. The first system was classical linear system. The second system was the linear system with a noise which had a different probability distribution than the Gaussian distribution and the last system was the system with nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Leveberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.

Anglický abstrakt

This paper focuses on application of a particle filter for online identification of non-Gaussian systems. Firstly, the Bayesian inference was described and then the particle filter was defined. The particle filter numerically solves a problem of a recursive Bayesian state estimator. Secondly, the parameters of the linear system and two types of the non-Gaussian systems were estimated by application of the particle filter. The first system was classical linear system. The second system was the linear system with a noise which had a different probability distribution than the Gaussian distribution and the last system was the system with nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Leveberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.

Dokumenty

BibTex


@inproceedings{BUT115312,
  author="Aleš {Lebeda}",
  title="Application of the particle filters for identification of the non-Gaussian systems",
  annote="This paper focuses on application of a particle filter for online identification of non-Gaussian systems. Firstly, the Bayesian inference was described and then the particle filter was defined. The particle filter numerically solves a problem of a recursive Bayesian state estimator. Secondly, the parameters of the linear system and two types of the non-Gaussian systems were estimated by application of the particle filter. The first system was classical linear system. The second system was the linear system with a noise which had a different probability distribution than the Gaussian distribution and the last system was the system with nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Leveberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.",
  address="University of Miskolc, Hungary",
  booktitle="Proceedings of the 16th International Carpathian Control Conference (ICCC2015)",
  chapter="115312",
  doi="10.1109/CarpathianCC.2015.7145089",
  howpublished="electronic, physical medium",
  institution="University of Miskolc, Hungary",
  year="2015",
  month="may",
  pages="282--285",
  publisher="University of Miskolc, Hungary",
  type="conference paper"
}