Identification of Mixed Linear/Nonlinear State-Space Models
2010 (English)In: Proceedings of the 49th IEEE Conference on Decision and Control, 2010, 6377-6382 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2010. 6377-6382 p.
Nonlinear system identification, Expectation maximization, Particle smoothing, Rao-Blackwellization
National CategoryControl Engineering Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-63502DOI: 10.1109/CDC.2010.5717191ISBN: 978-1-4244-7745-6OAI: oai:DiVA.org:liu-63502DiVA: diva2:380236
49th IEEE Conference on Decision and Control, Atlanta, GA, USA, 15-17 December, 2010
©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Fredrik Lindsten and Thomas B. Schön, Identification of Mixed Linear/Nonlinear State-Space Models, 2010, Proceedings of the 49th IEEE Conference on Decision and Control (CDC), 6377-6382.2010-12-222010-12-202013-07-09Bibliographically approved