Application of neural control to economic growth problems

The evolution of the freight transportation market is a complex phenomenon that can be described by means of suitable dynamic models. This model depends on a set of control variables (i.e., the percentage of carbon tax on the fuel cost, the operational cost coverages, and growth rates of the various transportation modes, such as railway, roadway, and waterway) that can be chosen in such a way as to minimize a given cost function (e.g., carbon emissions, public and private costs, fuel consumption, etc.). The problem has been addressed by searching for a feedback control law that can be approximated by means of the combination of both dynamic programming and neural networks. Simulation results with the afore-mentioned model are presented to demonstrate the effectiveness of the proposed method.