Pre-trained Neural Networks Used for Non-linear State Estimation
Enis Bayramoglu, Nils Andersen, Ole Ravn, Niels Kjølstad Poulsen · 2011
The paper focuses on nonlinear state estimation assuming non-Gaussian distributions of the states and the disturbances. The posterior distribution and the a posteriori distribution is described by a chosen family of parametric distributions. The state transformation then results in a transformation of the parameters 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-linear ties. The method can also be used to improve other parametric methods around regions with strong non-linear ties by including them inside the network.