Multilayer feedforward networks are universal approximators
Neural Networks
The Strength of Weak Learnability
Machine Learning
Optimal linear combinations of neural networks
Optimal linear combinations of neural networks
Machine Learning
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Ensembling neural networks: many could be better than all
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Design of structural modular neural networks with genetic algorithm
Advances in Engineering Software
Steel columns under fire: a neural network based strength model
Advances in Engineering Software
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
Design of ensemble neural network using entropy theory
Advances in Engineering Software
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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.