Radial basis functions applied to the classification of UV–visible spectra

Abstract This paper describes how to apply a neural network based in radial basis functions (RBFs) to classify multivariate data. The classification strategy was automatically implemented in a sequential injection analytical system. RBF neural network had some advantages over counterpropagation neural networks (CPNNs) when they are used in the same application: the classification error was reduced from 20% to 13%, the input variables (UV–visible spectra) did not have to be preprocessed and the training procedure was simpler.

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