Fundamentals in Neurocomputing

    Publikation: Working/Discussion PaperWU Working Paper

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    Neurocomputing - inspired from neuroscience - provides the potential of an alternative
    information processing paradigm that involves large interconnected networks of relatively
    simple and typically non-linear processing elements, so-called (artificial) neural networks.
    There has been a recent resurgence in the field of neural networks, caused by new net
    topologies and algorithms, and the belief that massive parallelism is essential for high
    peiformance in several research areas, especially in pattern recognition. This contribution
    provides a brief introduction to some basic features of neural networks by defining a neural
    network, reflecting current thinking about the processing that should be peiformed at each
    processing element of a neural network, discussing the general categories of training that are
    commonly used to adjust a neural network's weight vector, and finally by characterizing the
    backpropagation neural networ:k which is one of the most important historical developments
    in neurocomputing.- The contribution concludes with pointing to some hot topics for future
    research. It is hoped that this contribution will stimulate the study of neural networks in
    quantitative geography and regional science. (author's abstract)
    HerausgeberWU Vienna University of Economics and Business
    PublikationsstatusVeröffentlicht - 1 Feb. 1994


    ReiheDiscussion Papers of the Institute for Economic Geography and GIScience

    WU Working Paper Reihe

    • Discussion Papers of the Institute for Economic Geography and GIScience