Neural Network Modeling of Constrained Spatial Interaction Flows: Design, Estimation, and Performance Issues
提出一种模块化乘积单元神经网络架构，用于建模单约束空间交互流，以奥地利区域间电信流量数据验证，相比标准重力模型和两阶段神经网络，在泛化性能上更优。
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.