Absorption Spectra Analysis using Modified Self-Organizing Feature Maps
DOI:
https://doi.org/10.14738/tmlai.36.1155Keywords:
Self-Organizing Feature Map, Unsupervised Learning, Energies, Intensities, Chemical AbsorptionAbstract
This research demonstrates an application of a modified self-organizing feature map (SOFM) algorithm to analyze and discover the quality of chemical absorption spectrum data. By forming an NxN neural array from input features and varying the essential parameters of the algorithm, map recognition quality is increased at the expense of more computation. The features of this SOFM are based on absorption intensity variations with excitation wavelength. SOFMs are used to discern pattern similarities and differences between spectral data. A context tree allows individual features, or key numbers in the data, to be input and classifies the vector to the type of data that is most similar. This research also use the self-organizing map to enhance as well as visualize resultant classification efficiency through the use of watershed transformation.
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