Spectral high resolution feature selection for retrieval of combustion temperature profiles

Authors:
Esteban García-Cuesta;Inés M. Galván;Antonio J. de Castro
Affiliations:
Physics Department, Carlos III University -Avenida de la Universidad, Leganés (Madrid), Spain;Computer Science Department, Carlos III University -Avenida de la Universidad, Leganés (Madrid), Spain;Physics Department, Carlos III University -Avenida de la Universidad, Leganés (Madrid), Spain
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 1
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks

Application of Neural Networks to Atmospheric Pollutants Remote Sensing

IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II

Quantified Score

Hi-index	0.00

Visualization

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.