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.
|Title of host publication||The tenth International Conference on Machine Learning and Applications|
|Publication status||Published - 2011|
|Event||10th International Conference on Machine Learning and Applications (ICMLA 2011) - Honolulu, Hawaii, United States|
Duration: 18 Dec 2011 → 21 Dec 2011
Conference number: 10
|Conference||10th International Conference on Machine Learning and Applications (ICMLA 2011)|
|Period||18/12/2011 → 21/12/2011|