Particle Filter Approach to Nonlinear System Identification under Missing Observations with a Real Application
2009 (English)In: Proceedings of the 15th IFAC Symposium on System Identification, 2009, 810-815 p.Conference paper (Refereed)
This article reviews authors' recently developed algorithm for identification of nonlinear state-space models under missing observations and extends it to the case of unknown model structure. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. If the model structure is unknown, an approximation of it is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through a real application.
Place, publisher, year, edition, pages
2009. 810-815 p.
Maximum likelihood methods, Bayesian methods, Nonlinear System Identification
IdentifiersURN: urn:nbn:se:liu:diva-45379DOI: 10.3182/20090706-3-FR-2004.00134Local ID: 82351OAI: oai:DiVA.org:liu-45379DiVA: diva2:266241
15th IFAC Symposium on System Identification, Saint-Malo, France, July, 2009