A neural network metamodel approach to capital investment decision analysis

The potential use of backpropagation networks, cascade-correlation learning networks, and radial basis function networks in developing metamodels to assist in performing sensitivity analysis of capital investment decisions is examined. The neural network metamodel approach is illustrated through a case study. It is shown that the performance of backpropagation and cascade-correlation learning metamodels is comparable with the traditional polynomial regression metamodel