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articles: A global search procedure for parameter estimation in neural spatial interaction modelling

Author

Listed:
  • Manfred M. Fischer

    (Department of Economic and Social Geography, Wirtschaftsuniversität Wien, Augasse 2-6, A-1090 Vienna, Austria)

  • Katerina Hlavácková-Schindler

    (Institute for Urban and Regional Research, Austrian Academy of Sciences, Postgasse 7/4/2, A-1010 Vienna, Austria)

  • Martin Reismann

    (Department of Economic and Social Geography, Wirtschaftsuniversität Wien, Augasse 2-6, A-1090 Vienna, Austria)

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 behavior in real world applications. This article explores this method as an alternative to current practice for solving the parameter estimation task, and attempts to assess its 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.

Suggested Citation

  • Manfred M. Fischer & Katerina Hlavácková-Schindler & Martin Reismann, 1999. "articles: A global search procedure for parameter estimation in neural spatial interaction modelling," Papers in Regional Science, Springer;Regional Science Association International, vol. 78(2), pages 119-134.
    • Handle: RePEc:spr:presci:v:78:y:1999:i:2:p:119-134
    Note: Received: 11 November 1998
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    Citations

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    Cited by:

    1. Manfred M. Fischer, 2009. "Principles of Neural Spatial Interaction Modeling," Advances in Spatial Science, in: Michael Sonis & Geoffrey J. D. Hewings (ed.), Tool Kits in Regional Science, chapter 8, pages 199-214, Springer.
    2. Yee Leung & Xing-Bao Gao & Kai-Zhou Chen, 2004. "A Dual Neural Network for Solving Entropy-Maximising Models," Environment and Planning A, , vol. 36(5), pages 897-919, May.
    3. Fischer, Manfred M., 2006. "Neural Networks. A General Framework for Non-Linear Function Approximation," MPRA Paper 77776, University Library of Munich, Germany.
    4. Fischer, Manfred M. & Reismann, Martin & Hlavackova-Schindler, Katerina, 2000. "Evaluating Neural Spatial Interaction. Modelling By Bootstrapping," ERSA conference papers ersa00p370, European Regional Science Association.

    More about this item

    Keywords

    Neural spatial interaction modelling; global search; differential evolution; interregional telecommunications;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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