EconPapers    
Economics at your fingertips  
 

Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues

Manfred Fischer, Martin Reismann and Katerina Hlavackova–Schindler

Journal of Regional Science, 2003, vol. 43, issue 1, 35-61

Abstract: In this paper a novel modular product unit neural network architecture is presented to model singly constrained spatial interaction flows. The efficacy of the model approach is demonstrated for the origin constrained case of spatial interaction using Austrian interregional telecommunication traffic data. The model requires a global search procedure for parameter estimation, such as the Alopex procedure. A benchmark comparison against the standard origin constrained gravity model and the two–stage neural network approach, suggested by Openshaw (1998), illustrates the superiority of the proposed model in terms of the generalization performance measured by ARV and SRMSE.

Date: 2003
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1111/1467-9787.00288

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jregsc:v:43:y:2003:i:1:p:35-61

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0022-4146

Access Statistics for this article

Journal of Regional Science is currently edited by Marlon G. Boarnet, Matthew Kahn and Mark D. Partridge

More articles in Journal of Regional Science from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().

 
Page updated 2019-12-18
Handle: RePEc:bla:jregsc:v:43:y:2003:i:1:p:35-61