A sensitivity analysis of a back-propagation neural network for manufacturing process parameters

Back-propagation neural networks that represent specific process parameters in a composite board manufacturing process were analyzed to determine their sensitivity to network design and to the values of the learning parameters used in the back-propagation algorithm. The effects of the number of hidden layers, the number of nodes in a hidden layer, and the values of the learning rate and momentum factor were studied. Three network modification strategies were applied to evaluate their effect on the predictive capability of the network. The convergence criteria were tightened, the number of hidden nodes and hidden layers was increased. These modifications did not improve the predictive capability of the composite board networks.