Vertical Vector Fields and Neural Networks: An Application in Atmospheric Pollution Forecasting

Abstract

In this paper we look at the role that vertical fields can play in enhancing the performance of a feedforward neural network. Vertical fields help us to determine zones in the input space that are mapped onto the same output, they act in a similar way to kernels of linear mappings but in a nonlinear setting. In the paper we illustrate our ideas using data from a real application, namely forecasting atmospheric pollution for the town of Saint-Etienne in France.