Maximum Likelihood Estimation in Mixed Linear/Nonlinear State-Space Models
2010 (English)Report (Other academic)
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 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.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 8 p.
LiTH-ISY-R, ISSN 1400-3902 ; 2958
Nonlinear system identification- -Expectation maximization--Particle smoothing--Rao-Blackwellization
IdentifiersURN: urn:nbn:se:liu:diva-97596ISRN: LiTH-ISY-R-2958OAI: oai:DiVA.org:liu-97596DiVA: diva2:649227
FunderSwedish Foundation for Strategic Research Swedish Research Council