Non-Linear State Estimation Using Pre-Trained Neural Networks

被引：0

作者：

Bayramoglu, Enis ^{[1
]}

Andersen, Nils Axel ^{[1
]}

Ravn, Ole ^{[1
]}

Poulsen, Niels Kjolstad ^{[2
]}

机构：

[1] Tech Univ Denmark, Dept Elect Engn, Elektrovej DTU Bldg 326, DK-2800 Lyngby, Denmark

[2] Tech Univ Denmark, Dept Informat & Math Modeling, DK-2800 Lyngby, Denmark

来源：

2010 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL | 2010年

关键词：

FILTERS;

D O I：

10.1109/ISIC.2010.5612848

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents a method to track non-Gaussian parametric probability density functions under non-linear 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.

引用

页码：1509 / 1514

页数：6

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