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

NEUROCOMPUTING

卷:30

Article

Evans, DJ ; Cornford, D ; Nabney, IT

关键词: wind vector retrieval;    ERS-1 satellite;    probabilistic models;    mixture density networks;    neural networks;   

DOI  :  10.1016/S0925-2312(99)00138-1

来源: Elsevier

【 摘 要 】

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 for modelling 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. (C) 2000 Elsevier Science B.V. All rights reserved.

【 授权许可】

Free