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Data Categorization and Neural Pattern Recognition

Published online by Cambridge University Press:  02 July 2020

C. Gatts  and
A. Mariano
C. Gatts
Affiliation:
on leave from, PEMM/COPPE, Universidade Federal do Rio de Janeiro
A. Mariano
Affiliation:
Laboratôrio de Ciências Fisicas, Universidade Estadual do Norte Fluminense, Av. Alberto Lamego 2000, 28015-620 Campos Dos Goytacazes, RJ, Brasil.
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Extract

The natural ability of Artificial Neural Networks to perform pattern recognition tasks makes them a valuable tool in Electron Microscopy, especially when large data sets are involved. The application of Neural Pattern Recognition to HREM, although incipient, has already produced interesting results both for one dimensional spectra and 2D images.

In the case of ID spectra, e.g. a set of EELS spectra acquired during a line scan, given a “vigilance parameter” (which sets the threshold for the correlation between two spectra to be high enough to consider them as similar) an ART-like network can distribute the incoming spectra into classes of similarity, defining a standard representation for each class. In order to enhance the discrimination ability of the network, the standard representations are orthonormalized, allowing for subtle differences betwen spectra and peak overlapping to be resolved. The projection of the incoming vectors onto the basis vectors thus formed gives rise to a profile of the data set.

Type
Quantitative Analysis For Series of Spectra and Images: Getting The Most From Your Experimental Data
Information
Microscopy and Microanalysis , Volume 3 , Issue S2: Proceedings: Microscopy & Microanalysis '97, Microscopy Society of America 55th Annual Meeting, Microbeam Analysis Society 31st Annual Meeting, Histochemical Society 48th Annual Meeting, Cleveland, Ohio, August 10-14, 1997 , August 1997 , pp. 933 - 934
Copyright
Copyright © Microscopy Society of America 1997

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References

Gatts, C.et al.Ultramicroscopy 59 (1995) 809.10.1016/0304-3991(95)00031-UCrossRefGoogle Scholar
Gatts, C.et al.Acta Microscopica 4 (1995) 2.Google Scholar
Kosko, B., Neural Networks and Fuzzy Systems, Prentice Hall, 1992.Google Scholar
The authors grateful acknowledge the use of the facilities at the Max-Planck-Institut fur Metallforschung. This work was supported by CNPq and FAPERJ.Google Scholar