Structured neural network modelling of multi-valued functions for wind vector retrieval from satellite scatterometer measurements |
| |
Authors: | David J. Evans Dan Cornford Ian T. Nabney |
| |
Affiliation: | Neural Computing Research Group, Aston Triangle, Aston University, Birmingham B4 7ET, UK |
| |
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 for modelling 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 |
本文献已被 ScienceDirect 等数据库收录! |