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Learning of Radial Basis Function Networks: Experimental Results

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    0404841 - UIVT-O 20020210 RIV US eng C - Conference Paper (international conference)
    Neruda, Roman
    Learning of Radial Basis Function Networks: Experimental Results.
    Recent Advances in Computers, Computing and Communications. World Scientific and Engineering Society Press, 2002 - (Mastorakis, N.; Mladenov, V.), s. 241-246. ISBN 960-8052-62-9.
    [World Multi-Conference on Circuits, Systems, Communications and Computeers /6./. Rethymno (GR), 07.07.2002-12.07.2002]
    R&D Projects: GA ČR GA201/01/1192; GA AV ČR IAB1030006
    Institutional research plan: AV0Z1030915
    Keywords : radial basis function networks * hybrid learning * soft computing
    Subject RIV: BA - General Mathematics

    We present various learning methods for RBF networks. The standard gradient-based learning is augmented by the weighted norm adaptation. The three-step learning algorithm uses different unsupervised learning algorithms for setting the centroids. Two possible combinations with genetic learning algorithm are considered as well. All learning variants are thoroughly compared on two benchmark tasks.
    Permanent Link: http://hdl.handle.net/11104/0125071

     
     

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