Design of ensemble neural network using the Akaike information criterion

Ensemble neural networks are commonly used networks in many engineering applications due to its better generalization property. In this paper, an ensemble neural network algorithm is proposed based on the Akaike information criterion (AIC). The AIC-based ensemble neural network searches the best weight configuration of each component network first, and uses the AIC as an automating tool to find the best combination weights of the ensemble neural network. Two analytical functions—the peak function and the Friedman function are used first to assess the accuracy of the proposed ensemble approach. The verified approach is then applied to a material modeling problem—the stress–strain–time relationship of mudstones. These computational experiments have verified that the AIC-based ensemble neural network outperforms both the simple averaging ensemble neural network and the single component neural network.