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

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

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

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

Department of Computer Science

Research output: Other contribution

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 language	English
Publisher	Aston University
Publication status	Published - 1 Oct 1998

Keywords

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

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title = "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.",

keywords = "wind vector retrieval, ERS-1 satellite, probabilistic models, mixture density networks, neural networks",

author = "Evans, {David J.} and Dan Cornford and Nabney, {Ian T.}",

year = "1998",

month = oct,

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language = "English",

publisher = "Aston University",

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AU - Evans, David J.

AU - Cornford, Dan

AU - Nabney, Ian T.

PY - 1998/10/1

Y1 - 1998/10/1

N2 - 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.

AB - 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.

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

M3 - Other contribution

PB - Aston University

ER -