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Author(s): Buydens, L. | Wienke, D. |
Year: 1996
Citation: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS Volume: 32 Issue: 2 Pages: 151-164
Abstract: The FuzzyARTMAP algorithm is studied with respect to its usefulness for supervised chemical pattern recognition. The theory of this relatively complex artificial neural classifier is presented in detail for chemists. An instructive data set of moderate size, describing male and female participants in courses of chemometrics by their body measures, is used to demonstrate how FuzzyARTMAP works and what its basic properties are.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Models:
Fuzzy ARTMAP,
Author(s): Buydens, L. | Cammann, K. | Feldhoff, R. | Huth-Fehre, T. | Kantimm, T. | Wienke, D. | van den Broek, W. |
Year: 1996
Citation: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS Volume: 32 Issue: 2 Pages: 165-176
Abstract: The supervised working FuzzyARTMAP pattern recognition algorithm has been applied to automated identification of post-consumer plastics by near-infrared spectroscopy (NIRS). Experimentally, a remote operating parallel multisensor device, based on a rapid InGaAs diode array detector combined with new collimation optics, has been used. The laboratory setup allows on-line identification of more than 100 spectra per second. Internal parameter settings of FuzzyARTMAP were varied to explore their influence on the classifier s behavior. Discrimination results obtained were better than those from an optimized multilayer feedforward backpropagation artificial neural network (MLF-BP) and significantly better than those provided by the partial least squares method (PLS2). Additional advantages of FuzzyARTMAP compared to these two classifiers are a significantly higher speed of calibration, the chemical interpretability of network weight coefficients and a built-in detector against extrapolations.
Topics:
Machine Learning,
Applications:
Chemical Analysis,
Industrial Control,
Models:
Fuzzy ARTMAP,