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A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

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  • Manfred M. Fischer

    ()

  • Yee Leung

    ()

Abstract

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.

Suggested Citation

  • Manfred M. Fischer & Yee Leung, 1998. "A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data," ERSA conference papers ersa98p478, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa98p478
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    Cited by:

    1. Fischer, Manfred M., 2006. "Neural Networks. A General Framework for Non-Linear Function Approximation," MPRA Paper 77776, University Library of Munich, Germany.
    2. Fischer, Manfred M. & Reismann, Martin, 2002. "A Methodology for Neural Spatial Interaction Modeling," MPRA Paper 77794, University Library of Munich, Germany.
    3. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets," Experimental 0511001, EconWPA.
    4. Reggiani, Aura & Nijkamp, Peter & Sabella, Enrico, 2001. "New advances in spatial network modelling: Towards evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 128(2), pages 385-401, January.
    5. Haikonen, Arto, 2000. "Interregional Trade Flows In Finland:Research Methods And Some Empirical Evidence," ERSA conference papers ersa00p514, European Regional Science Association.
    6. Aura Reggiani & Peter Nijkamp & Enrico Sabella, 1998. "Evolutionary algorithms: Overview and applications to European transport," ERSA conference papers ersa98p412, European Regional Science Association.
    7. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    8. Manfred M. Fischer, 2003. "Principles of Neural Spatial Interaction Modeling," ERSA conference papers ersa03p526, European Regional Science Association.

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