Methods to improve prediction performance of ANN models

Artificial neural network (ANN) is a powerful tool and applied successfully in numerous fields. But there are still two limitations on its use. One is over-training, which occurs when the capacity of the ANN for training is too great because it is allowed too many training iterations. The other is that ANNs are not effective for extrapolation, which is sometimes very important because the existing data used to train an ANN do not necessarily cover the entire range. The two limitations degrade seriously the prediction performance of ANN models. In this paper, two practices are introduced to alleviate or overcome the negative effect of the limitations. Demonstrations based on these practices indicate that they are general and useful practices and can improve greatly the prediction performance of the resulting ANN models to make them really suitable for engineering applications.