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
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;
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-7745-6
DOI :
10.1109/CDC.2010.5717378