García-Cuesta, EstebanGalván, Inés M.Castro, Antonio J. de2009-06-222009-06-222006Intelligent Data Engineering and Automated Learning. IDEAL 2006. Berlin : Springer, 2006, p. 754-762ISBN 978-3-540-45485-40302-9743 (Print)1611-3349 (Online)http://hdl.handle.net/10016/4470Proceeding of: 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 (Burgos, Spain, September 20-23, 2006)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.text/plainapplication/pdfeng©SpringerSpectral high resolutionCombustion temperatureconference paperInformática10.1007/11875581_91open access754762Intelligent Data Engineering and Automated Learning. IDEAL 20064224