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.

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: 28 May 2024 07:06
Date Deposited: 29 Jul 2009 12:21
PURE Output Type: Technical report
Published Date: 1998-10-22
Authors: Evans, David J.
Cornford, Dan (ORCID Profile 0000-0001-8787-6758)
Nabney, Ian T. (ORCID Profile 0000-0003-1513-993X)

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