Multilayer perceptron as inverse model in a ground-based remote sensing temperature retrieval problem

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Show simple item record García-Cuesta, Esteban Galván, Inés M. Castro González, Antonio Jesús de 2009-06-03T10:00:32Z 2009-06-03T10:00:32Z 2008-02
dc.identifier.bibliographicCitation Engineering applications of artificial intelligence Feb 2008, vol. 21, n. 1, p. 26-34
dc.identifier.issn 0952-1976
dc.description.abstract In 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier
dc.subject.other Combustions
dc.subject.other Dimensionality reduction
dc.subject.other Ground base remote sensing
dc.subject.other Inverse models
dc.subject.other Neural networks
dc.subject.other Retrieval temperature
dc.subject.other Principal component analysis
dc.title Multilayer perceptron as inverse model in a ground-based remote sensing temperature retrieval problem
dc.type article PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Informática
dc.identifier.doi 10.1016/j.engappai.2007.03.005
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 26
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 34
dc.identifier.publicationtitle Engineering applications of artificial intelligence
dc.identifier.publicationvolume 21
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