Abstract
The computer-based interpretation of spectrometric data {/sub y(n)//sup /spl sim/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 {/sub x(n)//sup /spl circ/Tr/}=/spl Rscr/[{/sub y(n)//sup /spl sim/Tr/}, /sub p/spl Rscr//] 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.
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