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

R E Kalaba, J E Moore, R Xu and G J Chen
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R E Kalaba: Departments of Economics, Electrical Engineering, and Biomedical Engineering, University of Southern California, Los Angeles, CA 90089-0042
J E Moore: 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

Environment and Planning A, 1999, vol. 31, issue 3, 441-457

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.

Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:31:y:1999:i:3:p:441-457

DOI: 10.1068/a310441

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