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49th IEEE Conference on Decision and Control (CDC)

DOI: 10.1109/cdc.2010.5717378

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Estimation of general nonlinear state-space systems

Proceedings article published in 2010 by Brett Ninness, Adrian Wills, Thomas B. Schön ORCID
This paper is available in a repository.
This paper is available in a repository.

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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.