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https://www.um.edu.mt/library/oar/handle/123456789/29095| Title: | Particle filters for recursive model selection in linear and nonlinear system identification |
| Authors: | Kadirkamanathan, Visakan Jaward, M.H. Fabri, Simon G. Kadirkamanathan, M. |
| Keywords: | Linear control systems Nonlinear control theory Regression analysis Recursive functions |
| Issue Date: | 2000 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Citation: | Kadirkamanathan, V., Jaward, M. H., Fabri, S. G., & Kadirkamanathan, M. (2000). Particle filters for recursive model selection in linear and nonlinear system identification. 39th IEEE Conference on Decision and Control, Sydney. 2391-2396. |
| 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 auto-regressive (AR) and nonlinear auto-regressive (NAR) systems. |
| URI: | https://www.um.edu.mt/library/oar//handle/123456789/29095 |
| Appears in Collections: | Scholarly Works - FacEngSCE |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| Particle_filters_for_recursive_model_selection_in_linear_and_nonlinear_system_identification.pdf Restricted Access | 587.14 kB | Adobe PDF | View/Open Request a copy |
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