Title :
Application of the particle filters for identification of the non-Gaussian systems
Author_Institution :
Dept. of Control & Instrum., Brno Univ. of Technol., Brno, Czech Republic
Abstract :
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 particle filter. The first system was the 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 a nonlinearity. Thirdly, the parameters of the non-Gaussian systems were estimated with the gradient based method Levenberg-Marquardt. Finally, the results from the particle filter were compared with the results from the gradient based method Levenberg-Marquardt.
Keywords :
Bayes methods; Gaussian distribution; gradient methods; linear systems; particle filtering (numerical methods); state estimation; Bayesian inference; Gaussian distribution; gradient based Levenberg-Marquardt method; linear system; nonGaussian system identification; particle filters; probability distribution; recursive Bayesian state estimator; Atmospheric measurements; Bayes methods; Estimation; Linear systems; Noise; Noise measurement; Particle measurements; Bayesian inference; Levenberg-Marquardt; identification; non-Gaussian; particle filters;
Conference_Titel :
Carpathian Control Conference (ICCC), 2015 16th International
Conference_Location :
Szilvasvarad
Print_ISBN :
978-1-4799-7369-9
DOI :
10.1109/CarpathianCC.2015.7145089