Non-Linear State Estimation Using Pre-Trained Neural Networks

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Abstract

This article presents a method to track non-Gaussian parametric probability density functions under nonlinear transformations and posterior calculations. The optimal set of parameters for the transformed distribution is a function of the parameters for the prior distribution and any other variables effecting the transformation. This function is approximated by a neural network using offline training. The training is based on monte carlo sampling. A way to obtain parametric distributions of flexible shape to be used easily with these networks is also presented. The method can also be used to improve other parametric methods around regions with strong non-linearities by including them inside the network.
Original languageEnglish
Title of host publicationProceedings of the 2010 IEEE International Symposium on Intelligent Control
PublisherIEEE
Publication date2010
Pages1509-1514
ISBN (Print)978-1-4244-5360-3
DOIs
Publication statusPublished - 2010
EventIEEE International Symposium on Intelligent Control 2010 - Yokohama, Japan
Duration: 8 Sep 201010 Sep 2010
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5604772

Conference

ConferenceIEEE International Symposium on Intelligent Control 2010
CountryJapan
CityYokohama
Period08/09/201010/09/2010
Internet address

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