Application and performance analysis of neural networks for decision support in conceptual design

This paper analyzes the use of feedforward multilayer perceptron neural networks in the support of the decision process during the conceptual design phase of engineering systems. A user friendly software tool is proposed in order to increase the quality of the designers’ decisions by offering intuitive insights during the early design phase. The performance gain is evaluated through controlled experiments performed with non-experienced users.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.

► We develop a decision support tool based on multilayer perceptron neural networks.
► We explain architectural choices to obtain an intuitive decision support tool.
► We measure the performance gain with a series of case studies with and without the tool.
► We show that the use of such tool can help provide better initial design suggestions.
► Neural networks can help improve the performance at the conceptual design phase.