Particle filters for recursive model selection in linear and nonlinear system identification

Author

Kadirkamanathan, Visakan ; Jaward, Mohamed Hisham ; Fabri, S.G. ; Kadirkamanathan, M.

Author_Institution

Dept. of Autom. Control & Syst. Eng., Sheffield Univ., UK

Volume

3

fYear

2000

fDate

2000

Firstpage

2391

Abstract

Recursive model selection can be addressed within the Bayesian framework, the multiple model algorithm being one such approach for linear Gaussian systems. The recent advances in nonlinear non-Gaussian estimation with the sequential Monte Carlo algorithms, such as the particle filter, allow the application of Bayesian inference to the development of recursive model selection algorithms for general nonlinear non-Gaussian systems. Such an algorithm is developed in this paper and applied to a linear autoregressive and nonlinear autoregressive systems

Keywords

Bayes methods; Monte Carlo methods; autoregressive processes; filtering theory; linear systems; nonlinear systems; recursive estimation; state estimation; state-space methods; Bayesian inference; Monte Carlo method; autoregressive systems; identification; linear system; nonlinear system; parameter estimation; particle filtering; recursive model selection; state estimation; state space method; Bayesian methods; Gaussian noise; Monte Carlo methods; Nonlinear systems; Parameter estimation; Particle filters; Power system modeling; Recursive estimation; State estimation; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on

Conference_Location

Sydney, NSW

ISSN

0191-2216

Print_ISBN

0-7803-6638-7

Type

conf

DOI

10.1109/CDC.2000.914157

Filename

914157