Kohonen and counterpropagation artificial neural networks in analytical chemistry

The principles of the Kohonen and counterpropagation artificial neural network (K-ANN and CP-ANN) learning strategy is described. The use of both methods (with the emphasis on CP-ANNs) is explained with several examples from analytical chemistry. The problems discussed in this presentation are: sele...

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Bibliographic Details
Published in:Chemometrics and intelligent laboratory systems 1997-08, Vol.38 (1), p.1-23
Main Authors: Zupan, Jure, Novič, Marjana, Ruisánchez, Itziar
Format: Article
Language:English
Subjects:
Counterpropagation network
Kohonen network
Neural networks
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Summary:The principles of the Kohonen and counterpropagation artificial neural network (K-ANN and CP-ANN) learning strategy is described. The use of both methods (with the emphasis on CP-ANNs) is explained with several examples from analytical chemistry. The problems discussed in this presentation are: selection of a set of representative objects from a large number of multi-variate measurements, clustering of multi-variate experiments (multi-component analyses), generation of logical ‘if-then’ rules for an automatic decision making process, automatic evaluation of the quality of spectra based on their shape, spectra recording, quantitative decisions using weight maps, multi-variate modelling of a property, generation of multi-variate response surfaces from a generated CP-ANN model, and estimation of missing variable-values.
ISSN:0169-7439
1873-3239
DOI:10.1016/S0169-7439(97)00030-0