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Spectral High Resolution Feature Selection for Retrieval of Combustion Temperature Profiles

  • Esteban García-Cuesta
  • Inés M. Galván
  • Antonio J. de Castro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)

Abstract

The use of high spectral resolution measurements to obtain a retrieval of certain physical properties related with the radiative transfer of energy leads a priori to a better accuracy. But this improvement in accuracy is not easy to achieve due to the great amount of data which makes difficult any treatment over it and it’s redundancies. To solve this problem, a pick selection based on principal component analysis has been adopted in order to make the mandatory feature selection over the different channels. In this paper, the capability to retrieve the temperature profile in a combustion environment using neural networks jointly with this spectral high resolution feature selection method is studied.

Keywords

Feature Selection Radiative Transfer Inverse Model Neural Network Technique High Resolution Measurement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Esteban García-Cuesta
    • 1
  • Inés M. Galván
    • 2
  • Antonio J. de Castro
    • 1
  1. 1.Physics DepartmentCarlos III University -Avenida de la UniversidadLeganés (Madrid)Spain
  2. 2.Computer Science DepartmentCarlos III University -Avenida de la UniversidadLeganés (Madrid)Spain

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