Reliable modeling of chemical duarability of high level waste glass using bootstrap aggregated neural networks

Abstract

Modeling chemical durability of high level waste glass for nuclear waste processing using bootstrap aggregated neural networks is studied in this paper. In order to overcome the difficulty in developing detailed mechanistic models, data driven neural network models are developed from experimental data. A key issue in building neural network models is that model generalization capability cannot be guaranteed due to the potential over-fitting problem and the limitation in the training data. In order to enhance model generalization, bootstrap aggregated neural networks are used in this study. Multiple neural network models are developed from bootstrap resampling replications of the original training data and are combined to give the final prediction. Application results show that accurate and reliable models can be developed using bootstrap aggregated neural networks.

Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks

“…Bootstrap aggregated neural network was proposed by Zhang [19]. It is shown that this type of aggregated models can achieve great performance in many applications with improved robustness [20][21][22][23][24]. Based on the idea of stacking several networks to enhance model performance, many approaches were proposed in recent years.…”