Quantitative multivariate analysis with artificial neural networks
Resource
Bioelectromagnetism, 1998. Proceedings of the 2nd International Conference on
Journal
2nd International Conference on Bioelectromagnetism
Pages
-
Date Issued
1998-02
Date
1998-02
Author(s)
DOI
N/A
Abstract
Quantitative interpretation of spectra can be achieved by using artificial neural networks with multi-layer architecture. Both back-propagation (BP) and radial basis function (RBF) are implemented and tested with raw absorption spectra and normalized spectra of glucose solutions in MATLAB. Simulation results showed that the partial least square (PLS) method can have a better performance with small number in the calibration set. However, with increasing size of data set, as in the cross validation method, RBF and BP have better performance. With optimal spreading factor, RBF can have the same degree of accuracy but significantly faster convergent speed comparing to BP. The normalization scheme can also significantly affect the performance of both RBF and BP.
Type
conference paper
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