Pre-Trained Neural Networks used for Non-Linear State Estimation

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Abstract

The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the aposteriori distribution is described by a chosen family of paramtric distributions. The state transformation then results in a transformation of the paramters in the distribution. This transformation is approximated by a neural network using offline training, which is based on monte carlo sampling. In the paper, there will also be presented a method to construct a flexible distributions well suited for covering the effect of the non-linearities. The method can also be used to improve other parametric methods around regions with strong nonlinearities by including them inside the network.
Original languageEnglish
Title of host publicationThe tenth International Conference on Machine Learning and Applications
PublisherIEEE
Publication date2011
DOIs
Publication statusPublished - 2011
Event10th International Conference on Machine Learning and Applications (ICMLA 2011) - Honolulu, Hawaii, United States
Duration: 18 Dec 201121 Dec 2011
Conference number: 10

Conference

Conference10th International Conference on Machine Learning and Applications (ICMLA 2011)
Number10
CountryUnited States
CityHonolulu, Hawaii
Period18/12/201121/12/2011

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