DocumentCode :
2571829
Title :
Estimation of general nonlinear state-space systems
Author :
Ninness, Brett ; Wills, Adrian ; Schön, Thomas B.
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Univ. of Newcastle, Callaghan, NSW, Australia
fYear :
2010
fDate :
15-17 Dec. 2010
Firstpage :
6371
Lastpage :
6376
Abstract :
This paper presents a novel approach to the estimation of a general class of dynamic nonlinear system models. The main contribution is the use of a tool from mathematical statistics, known as Fishers´ identity, to establish how so-called “particle smoothing” methods may be employed to compute gradients of maximum-likelihood and associated prediction error cost criteria.
Keywords :
maximum likelihood estimation; state-space methods; statistics; Fishers identity; dynamic nonlinear system model; general nonlinear state-space system; mathematical statistics; maximum likelihood; particle smoothing; prediction error cost criteria; Approximation methods; Computational modeling; Markov processes; Mathematical model; Maximum likelihood estimation; Yttrium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
ISSN :
0743-1546
Print_ISBN :
978-1-4244-7745-6
Type :
conf
DOI :
10.1109/CDC.2010.5717378
Filename :
5717378
Link To Document :
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