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

The computer-based interpretation of spectrometric data {_y(n) ^~Tr} is aimed at identification of the main components of an analyzed substance. The first step of interpretation consists in estimation of its spectrum using an operator of (generalized) deconvolution {_x(n)^ˆTr}=ℛ[{_y(n) ^~Tr}, _pℛ] were p, is a vector of parameters to be estimated during calibration of the spectrometer. Several new structures of this operator, based on combination of the Cauchy filter with an RBF-type neural network, are proposed and studied in this paper using both synthetic and real-world spectro-photometric data. Their superiority over existing algorithms for spectrum reconstruction is demonstrated