### Abstract

Original language | English |
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Place of Publication | Birmingham |

Publisher | Aston University |

Number of pages | 8 |

ISBN (Print) | NCRG/98/022 |

Publication status | Published - 22 Oct 1998 |

### Fingerprint

### Keywords

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

### Cite this

*Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements*. Birmingham: Aston University.

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**Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements.** / Evans, David J.; Cornford, Dan; Nabney, Ian T.

Research output: Working paper › Technical report

TY - UNPB

T1 - Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

AU - Evans, David J.

AU - Cornford, Dan

AU - Nabney, Ian T.

PY - 1998/10/22

Y1 - 1998/10/22

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

KW - ERS-1 satellite

KW - probabilistic models

KW - mixture density networks

KW - neural networks

M3 - Technical report

SN - NCRG/98/022

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

PB - Aston University

CY - Birmingham

ER -