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• W2022192511 endingPage "324" @default.
• W2022192511 startingPage "314" @default.
• W2022192511 abstract "This article presents an algorithm for identification of nonlinear state-space models when the “true” model structure of a process is unknown. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. An approximation of the model structure 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 extended to handle missing observations and illustrated through a real application." @default.
• W2022192511 created "2016-06-24" @default.
• W2022192511 creator A5008908321 @default.
• W2022192511 date "2010-03-01" @default.
• W2022192511 modified "2023-09-25" @default.
• W2022192511 title "Nonlinear system identification under missing observations: The case of unknown model structure" @default.
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• W2022192511 doi "https://doi.org/10.1016/j.jprocont.2009.12.008" @default.
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