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

Proceedings of the IEEE Conference on Decision and Control

Volumn 3, Issue , 2000, Pages 2391-2396

  Kadirkamanathan, V ^a   Jaward, M H ^a   Fabri, S G ^b   Kadirkamanathan, M ^c  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITY OF MALTA   (Malta)

^c Dragon Systems UK Ltd   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; MONTE CARLO METHODS; NONLINEAR CONTROL SYSTEMS; RANDOM PROCESSES; REGRESSION ANALYSIS;

BAYESIAN INFERENCE; LINEAR AUTOREGRESSIVE SYSTEMS; LINEAR GAUSSIAN SYSTEMS; NONLINEAR AUTOREGRESSIVE SYSTEMS; PARTICLE FILTERS; RECURSIVE MODEL SELECTION;

RECURSIVE FUNCTIONS;

EID: 0034439374     PISSN: 01912216     EISSN: None     Source Type: Journal    
DOI: 10.1109/CDC.2000.914157     Document Type: Article

References (13)

1. Bayesian computational approaches to model selection
2. Recursive Bayesian estimation: Navigation and tracking applications
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4. Sequential Monte Carlo methods in filter theory
5. Novel approach to nonlinear/non-gaussian Bayesian state estimation
6. Monte Carlo simulation techniques to estimation the conditional expectation in multi-stage non-linear filtering estimation
7. Sequential Monte Carlo methods for dynamic systems
8. Recursive nonlinear identification using multiple model algorithm
9. A self-organising state-space model
10. Identification of ARX models with Markovian parameters