A Gobal Search Procedure for Parameter Estimation in Neural Spatial Interaction Modelling

Manfred M. Fischer, Katarina Hlavácková-Schindler, Martin Reismann

    Publication: Working/Discussion PaperWU Working Paper

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    Abstract

    Parameter estimation is one of the central issues in neural spatial interaction modelling.
    Current practice is dominated by gradient based local minimization techniques. They
    find local minima efficiently and work best in unimodal minimization problems, but
    can get trapped in multimodal problems. Global search procedures provide an
    alternative optimization scheme that allows to escape from local minima. Differential
    evolution has been recently introduced as an efficient direct search method for
    optimizing real-valued multi-modal objective functions (Storn and Price 1997). The
    method is conceptually simple and attractive, but little is known about its behaviour in
    real world applications. This paper explores this method as an alternative to current
    practice for solving the parameter estimation task, and attempts to assess ist robustness,
    measured in terms of in-sample and out-of-sample performance. A benchmark
    comparison against backpropagation of conjugate gradients is based on Austrian
    interregional telecommunication traffic data. (authors' abstract)
    Original languageEnglish
    Place of PublicationVienna
    PublisherWU Vienna University of Economics and Business
    DOIs
    Publication statusPublished - 1 Sept 1998

    Publication series

    SeriesDiscussion Papers of the Institute for Economic Geography and GIScience
    Number63/98

    WU Working Paper Series

    • Discussion Papers of the Institute for Economic Geography and GIScience

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