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Cauchy filters versus neural networks when applied for reconstruction of absorption spectra

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2 Author(s)
P. Sprzeczak ; Inst. of Radioelectronics, Warsaw Univ. of Technol., Poland ; R. Z. Morawski

The computer-based interpretation of data {y˜nTr}, acquired by means of an optical spectrometer, is aimed at identification of the main components of an analyzed substance. The first step of interpretation consists in estimation of its spectrum {xnTr} using an operator of (generalized) deconvolution {xˆnTr} = R[{y˜nTr}; pR], where pR is a vector of parameters to be estimated during calibration of the spectrometer. Several new structures of this operator, based on combinations of a nonlinear transformation or the Cauchy filter and of an radial-basis-function-type neural network, are proposed and studied in this paper using both synthetic and real-world spectrophotometric data. Their superiority over existing operators for spectrum reconstruction is demonstrated with respect to uncertainty of reconstruction.

Published in:

IEEE Transactions on Instrumentation and Measurement  (Volume:51 ,  Issue: 4 )