Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

David J. Evans, Dan Cornford, Ian T. Nabney

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)23-30
Number of pages8
JournalNeurocomputing
Volume30
Issue number1-4
Early online date4 Oct 1999
DOIs
Publication statusPublished - 1 Jan 2000

Bibliographical note

See http://eprints.aston.ac.uk/1412/

Keywords

  • wind vector retrieval, ERS-1 satellite, probabilistic models, mixture density networks, neural networks

Fingerprint Dive into the research topics of 'Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements'. Together they form a unique fingerprint.

Cite this