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Nonlinear Estimation with Associative Memories and Machine Evaluation of Derivatives: An Application to Calibrating Spatial Interaction Models

Author

Listed:
  • R E Kalaba

    (Departments of Economics, Electrical Engineering, and Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-0042)

  • J E Moore II

    (Department of Civil Engineering, and School of Urban Planning and Development, University of Southern California, Los Angeles, CA 90089-0042)

  • R Xu

    (Department of Economics, State University of New York at Buffalo, Amherst, New York 14260)

  • G J Chen

    (School of Urban Planning and Development, University of Southern California, Los Angeles, CA 90089-0042)

Abstract

In this paper we apply the theory of linear associative memories in producing initial parameter estimates for nonlinear iterative approaches. We also propose the use of FEED (Fast and Efficient Evaluation of Derivatives) to evaluate partial derivatives of functions encountered in nonlinear estimation. Suggested methods are presented in the context of calibrating spatial interaction models and are illustrated through numerical examples.

Suggested Citation

  • R E Kalaba & J E Moore II & R Xu & G J Chen, 1999. "Nonlinear Estimation with Associative Memories and Machine Evaluation of Derivatives: An Application to Calibrating Spatial Interaction Models," Environment and Planning A, , vol. 31(3), pages 441-457, March.
    • Handle: RePEc:sae:envira:v:31:y:1999:i:3:p:441-457
    DOI: 10.1068/a310441
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    References listed on IDEAS

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    1. Kalaba, R. & Lichtenstein, Z. & Tesfatsion, L., 1989. "Linear And Nonlinear Associative Memories For Parameter Estimation," Papers m8913, Southern California - Department of Economics.
    2. Fischer, Manfred M. & Gopal, Sucharita, 1994. "Artificial Neural Networks. A New Approach to Modelling Interregional Telecommunication Flows," MPRA Paper 77822, University Library of Munich, Germany.
    3. Openshaw, Stan, 1992. "Some Suggestions Concerning the Development of Artificial Intelligence Tools for Spatial Modelling and Analysis in GIS," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 26(1), pages 35-51, April.
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