Effects of noise-reduction on neural function approximation

Noise disturbance in training data prevents a good approximation of a function by neural networks. To achieve better approximation results we combine neural networks with noise reduction algorithms. We compare different methods to distinguish between samples with high noise level (outliers) in a dataset and samples with low noise level. Drawbacks of common outlier detection approaches are analysed and a new approach is defined which increases the quality of network function approximation. We demonstrate the effects of noise reduction on artificial datasets and on real data from the process control domain.