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A representative airglow volume emission profile from rocket-borne photometer data by an artificial neural network technique

doi: 10.6062/jcis.2011.02.02.0037(Free PDF)


F.C. de Meneses, E.H. Shiguemori, P. Muralikrishna and B.R. Clemesha


ABSTRACT The inverse problem to retrieve useful airglow volume emission rate profiles from rocket-borne photometer measurements has been solved by adopting the well-characterized spectral photometric methods. An alternative recovery method based on artificial neural network (ANN) is presented. A multilayer perceptron neural network was trained with available experimental and synthetic data. Integrated emissions profiles measured by a rocket experiment were taken as the input data. From the results obtained in this work, it may be concluded that the ANN technique is a convenient tool to recover volume emission rate profiles.


applied computing in space and environmental sciences, neural networks, inverse problem, airglow, rocket.


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