RBF neural networks for solving the inverse problem of backscattering spectra |
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Authors: | Michael M Li Brijesh Verma Xiaolong Fan Kevin Tickle |
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Affiliation: | (1) School of Computing Sciences, Faculty of Business and Informatics, Central Queensland University, Rockhampton, QLD, 4702, Australia |
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Abstract: | This paper investigates a new method to solve the inverse problem of Rutherford backscattering (RBS) data. The inverse problem
is to determine the sample structure information from measured spectra, which can be defined as a function approximation problem.
We propose using radial basis function (RBF) neural networks to approximate an inverse function. Each RBS spectrum, which
may contain up to 128 data points, is compressed by the principal component analysis, so that the dimensionality of input
data and complexity of the network are reduced significantly. Our theoretical consideration is tested by numerical experiments
with the example of the SiGe thin film sample and corresponding backscattering spectra. A comparison of the RBF method with
multilayer perceptrons reveals that the former has better performance in extracting structural information from spectra. Furthermore,
the proposed method can handle redundancies properly, which are caused by the constraint of output variables. This study is
the first method based on RBF to deal with the inverse RBS data analysis problem. |
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Keywords: | Radial basis function Inverse problems Neural networks RBS Spectral data analysis |
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