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

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
DOIs
Publication statusPublished - 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

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    • 1 Technical report

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

    Evans, D. J., Cornford, D. & Nabney, I. T., 22 Oct 1998, Birmingham: Aston University, 8 p.

    Research output: Working paperTechnical report

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