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Kalamatianos, Dimitrios, Anastasiadis, Aristoklis D. and Liatsis, Panos (2009) A nonextensive method for spectroscopic data analysis with artificial neural networks. Brazilian Journal of Physics, 39 (2A). pp. 488-494. ISSN 0103-9733
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Abstract
In this paper we apply an evolving stochastic method to construct simple and effective Artificial Neural Networks,
based on the theory of Tsallis statistical mechanics. Our aim is to establish an automatic process for
building a smaller network with high classification performance. We aim to assess the utility of the method
based on statistical mechanics for the estimation of transparent coating material on security papers and cholesterol
levels in blood samples. Our experimental study verifies that there are indeed improvements in the overall
performance in terms of classification success and at the size of network compared to other efficient backpropagation
learning methods.
| Item Type: | Article |
|---|---|
| Keywords: | Nonextensive statistical mechanics; Neural networks; Pattern classification; Spectroscopy; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 2117 |
| Depositing User: | Dimitris Kalamatianos |
| Date Deposited: | 22 Sep 2010 15:26 |
| Journal or Publication Title: | Brazilian Journal of Physics |
| Publisher: | Sociedade Brasileira de Fisica |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/2117 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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