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CDC
2010
IEEE

Identification of mixed linear/nonlinear state-space models

10 years 6 months ago
Identification of mixed linear/nonlinear state-space models
The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using a so called Rao-Blackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study.
Fredrik Lindsten, Thomas B. Schön
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where CDC
Authors Fredrik Lindsten, Thomas B. Schön
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