@techreport{838cb4bde32d40799ef2af2d58bd8f38,
title = "Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements",
abstract = "A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.",
keywords = "wind vector retrieval, ERS-1 satellite, probabilistic models, mixture density networks, neural networks",
author = "Evans, {David J.} and Dan Cornford and Nabney, {Ian T.}",
year = "1998",
month = oct,
day = "22",
language = "English",
isbn = "NCRG/98/022",
publisher = "Aston University",
type = "WorkingPaper",
institution = "Aston University",
}