A GA-Optimised Ensemble Neural Network Model For Charpy Impact Energy Predictions

An ensemble modelling strategy, which is based on the genetic algorithm neural network (GA-NN) optimisation, is developed in this paper. A diversity index, defined by the dissimilarity between the current neural network (NN) and the set of existing NNs, is first introduced to facilitate the qualification of the current NN for being included in the ensemble network. A fitness-weighted assemble scheme is then proposed to form the GA-NN ensemble model. The unique advantage of this ensemble modelling scheme is its high efficiency, thanks to the full exploitation of information generated during the GA-NN optimisation. Preliminary results obtained for the prediction of the Charpy impact energy of heat-treated steel are promising, with the model performance being significantly improved as compared to previous modelling results.