Neural networks and the bias/variance dilemma
Neural Computation
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
Introduction to Neural Networks with Java
Introduction to Neural Networks with Java
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Artificial neural network-based model for predicting VO2max from a submaximal exercise test
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
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.