Research Output per year

### 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 |
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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 |

### 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.

## Research Output

- 1 Article

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

Evans, D. J., Cornford, D. & Nabney, I. T., Jan 2000, In : Neurocomputing. 30, 1-4, p. 23-30 8 p.Research output: Contribution to journal › Article

## Cite this

Evans, D. J., Cornford, D., & Nabney, I. T. (1998).

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