Improving Multi Step-Ahead Model Prediction through Backward Elimination Method in Multiple Neural Networks Combination

Abstract

Combining multiple neural networks appears to be a very promising approach in improving neural network generalization since it is very difficult, if not impossible, to develop a solution that is close to global optimum using single neural network. In this paper, individual networks are developed from bootstrap re-sample of the original training and testing data sets. Instead of combining all the developed networks, this paper proposed backward elimination. In backward elimination, all the individual networks are initially aggregated and some of the individual networks are then gradually eliminated until the aggregated network error on the original training and testing data sets cannot be further reduced. The proposed techniques are applied to nonlinear process modeling and application results demonstrate that the proposed techniques can significantly improve model performance better than aggregating all the individual networks.