MURAL - Maynooth University Research Archive Library

    A nonextensive method for spectroscopic data analysis with artificial neural networks

    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

    [img] Download (1MB)

    Share your research

    Twitter Facebook LinkedIn GooglePlus Email more...

    Add this article to your Mendeley library


    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
    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

    Repository Staff Only(login required)

    View Item Item control page


    Downloads per month over past year

    Origin of downloads