This paper proposes using the prediction capabilities of Artificial Neural Networks to recommend design solutions based on earlier successful designs. It implements a feedforward multilayer perceptron architecture with sigmoid activation functions, and uses the Levenberg-Marquardt training algorithm for weight matrix update. The network complexity was abstracted to ease the utilization by non-expert users. Different stopping conditions were developed including one where the training and testing error divergence is monitored to tackle the bias/variance dilemma.
The advantages of this approach for decision support were measured through a set of six different case studies such as gaseous automobile emissions prediction based on institutional data, or the choice of a launcher for a specific space mission. The decision support tool generated quality performance gains between 13%and 88%for examples ranging from simple continuous single variable to complex discrete multivariate problems.