Radial basis functions applied to the classification of UV–visible spectra
| Autor: | Itziar Ruisánchez, F.X. Rius, A. Pulido |
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| Rok vydání: | 1999 |
| Předmět: |
Multivariate statistics
Radial basis function network Artificial neural network business.industry Chemistry MathematicsofComputing_NUMERICALANALYSIS Pattern recognition Stellar classification Biochemistry Analytical Chemistry Chemometrics ComputingMethodologies_PATTERNRECOGNITION Uv visible spectra Statistics Principal component analysis Environmental Chemistry Radial basis function Artificial intelligence business Spectroscopy |
| Zdroj: | Analytica Chimica Acta. 388:273-281 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/s0003-2670(99)00082-3 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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