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

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.