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

Evans, David J., Cornford, Dan and Nabney, Ian T. (2000). Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements. Neurocomputing, 30 (1-4), pp. 23-30.

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.

Publication DOI: https://doi.org/10.1016/S0925-2312(99)00138-1
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: See http://eprints.aston.ac.uk/1412/
Uncontrolled Keywords: wind vector retrieval,ERS-1 satellite,probabilistic models,mixture density networks,neural networks
Published Date: 2000-01
Authors: Evans, David J.
Cornford, Dan ( 0000-0001-8787-6758)
Nabney, Ian T. ( 0000-0003-1513-993X)

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