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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/70269

    Title: A novel class of neural networks with quadratic junctions
    Authors: DeClaris, Nicholas;Su, Mu-chun
    Contributors: 淡江大學電機工程學系
    Date: 1991-10
    Issue Date: 2011-10-23 21:07:04 (UTC+8)
    Publisher: IEEE
    Abstract: The authors discuss the architecture and training properties of a multilayer feedforward neural network class that uses quadratic junctions in a neural architecture that uses effectively the backpropagation learning algorithm given by P.J. Werbos (1989). Both the architecture of the quadratic junctions and the backpropagation were adopted so as to endow the networks with appealing training properties (under supervision) and acceptable generalizations. Complexity and learning aspects of this class are examined and compared with traditional networks that use linear junctions.
    Relation: Proceedings of the IEEE international conferences on systems, man, and cybernetics, v.3,p.p1557 - 1562
    DOI: 10.1109/ICSMC.1991.169910
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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