García-Cuesta, EstebanGalván, Inés M.Castro González, Antonio Jesús de2009-06-032009-06-032008-02Engineering applications of artificial intelligence Feb 2008, vol. 21, n. 1, p. 26-340952-1976https://hdl.handle.net/10016/4336In this paper, a combustion temperature retrieval approximation for high-resolution infrared ground-based measurements has been developed based on a multilayer perceptron (MLP) technique. The introduction of a selection subset of features is mandatory due to the problems related to the high dimensionality data and the worse performance of MLPs with this high input dimensionality. Principal component analysis is used to reduce the input data dimensionality, selecting the physically important features in order to improve MLP performance. The use of a priori physical information over other methods in the chosen feature’s phase has been tested and has appeared jointly with the MLP technique as a good alternative for this problem.application/pdfeng© ElsevierCombustionsDimensionality reductionGround base remote sensingInverse modelsNeural networksRetrieval temperaturePrincipal component analysisresearch articleInformática10.1016/j.engappai.2007.03.005open access26134Engineering applications of artificial intelligence21