Evans, David J., Cornford, Dan and Nabney, Ian T. (1998). Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements. Technical Report. Aston University, Birmingham.
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.
Divisions: | ?? 50811700Jl ?? College of Engineering & Physical Sciences > Systems analytics research institute (SARI) |
---|---|
Uncontrolled Keywords: | wind vector retrieval,ERS-1 satellite,probabilistic models,mixture density networks,neural networks |
ISBN: | NCRG/98/022 |
Last Modified: | 05 Aug 2024 09:07 |
Date Deposited: | 29 Jul 2009 12:21 | PURE Output Type: | Technical report |
Published Date: | 1998-10-22 |
Authors: |
Evans, David J.
Cornford, Dan ( ![]() Nabney, Ian T. ( ![]() |