Absorption Spectra Analysis using Modified Self-Organizing Feature Maps
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.
(1) T. G. Schaaff, R. L. Whetten, “Giant gold-glutathione cluster compounds: Intense optical activity in metal-based transitions,” J. Phys. Chem. B, 104, 2630-2641 (2000).
(2) A. Collings, K. Athanassenas, D. Lacombe, D. M. Rayner, and P. A. Hackett, “Optical absorption spectra of Au7, Au9, Au11, and Au13, and their cations: Gold clusters with 6, 7, 8, 9, 10, 11, 12, and 13 s‐electrons,” J. Chem. Phys. 101, 3506 (1994).
(3) S. Lecoultre, A. Rydlo, C. Felix, J. Buttet, S. Gilb, and W. Harbich, “UV-visible absorption of small gold clusters in neon: Au-n (n=1-5 and 7-9), “ J. Chem. Phys. 134, 074302 (2011).
(4) A. I. Krylov, Annu. “Equation-of-motion coupled-cluster methods for open-shell and electronically excited species: The Hitchhiker's guide to Fock space ,” Rev. Phys. Chem. 59, 433 (2008).
(5) T. Kohonen, Self-Organizing Maps. Springer, 2001, BerlinWhetten RL.
(6) J. Vesanto and Alhoniemi, E. “Clustering of the self-Organizing Map.” IEEE Transactions on Neural Networks, vol. 11, No. 3, May 2000. Neural Networks Res. Centre, Helsinki Univ. of Technol., Esposo, Finland.
(7) Bradley, Matthew, Kay Jantharasorn, Keith Jones, and Dr. M. Zohdy. “Self Organized Neural Networks Applied to Animal Communications.” REU Report, 2008.
(8) Su, Mu-Chun and Chung, Hsiao-Te. “Fast Self-Organizing Feature Map Algorithm.” IEEE Transactions on Neural Networks. Vol. 11, no. 3, May 2000, pp. 721-733.
(9) T. Bryant, M. Hodges, and M. Zohdy. “Modified Self-Organizing Maps for Engine Health Diagnostics”, International Journal of Computing and Information Technology. Vol. 3, no. 2, March 2014.
(10) T. Bryant and M. Zohdy, “Noise Signal identification by Modified Self-Organizing Maps”, International Journal of Computing and Information Technology. vol. 2, no. 6, November 2013.