Kalamatianos, Dimitrios and 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
URI:	http://www.scielo.br/scielo.php?script=s...
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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