A novel modular product unit neural network for modelling constrained spatial interaction flows

Author

Fischer, Manfred M.

Author_Institution

Vienna Univ. of Econ. & Bus. Adm., Austria

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1215

Lastpage

1220

Abstract

A novel product unit neural network approach is presented for modelling origin constrained spatial interaction flows. We adopt the Alopex procedure, a global search procedure, in combination with the bootstrapping pairs approach to address the issue of maximum likelihood estimation of the parameters. A benchmark comparison illustrates the generalisation performance of the model approach in terms of Kullback and Leibler´s information criterion

Keywords

constraint handling; maximum likelihood estimation; neural nets; parameter estimation; search problems; Alopex procedure; benchmark comparison; bootstrapping pairs approach; constrained spatial interaction flows; global search procedure; maximum likelihood estimation; modular product unit neural network; origin constrained spatial interaction flows; parameters estimation; Biological neural networks; Electronic mail; Environmental economics; Gravity; Maximum likelihood estimation; Neural networks; Parameter estimation; Particle measurements; Power generation economics; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7282-4

Type

conf

DOI

10.1109/CEC.2002.1004416

Filename

1004416