A simultaneous use of artifical neural networks (ANN) and principal components has considered for remote sensing of temperature and composition profiles of the atmosphere. Some modification of ANN method has been offered based on minimisation of final product errors. An example of applications of the modification is adduced. An important advantage of the new approach has shown in speed of training of ANN and in profile precision.
solution of atmospheric optic inverse problems, meteorological sounding of atmosphere, principal components, artifical neural network
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