Sample Normalization Algorithm of Neural Network Based on Fuzzy Rough Set Theory

A novel sample normalization algorithm based on fuzzy rough set theory is proposed to avoid the longtime training of neural network classifier caused by the smaller distances between samples of different classes. Firstly, the samples are discretized based on rough set theory. Then, according to the distance differences between their discretized samples and two class samples and the energy differences between the two class samples, the original samples are extended or contracted based on fuzzy set theory. Then, the samples extended or contracted are normalized. Finally, the normalized samples are used to train the neural network. The method is analyzed with an example of faulty line detection for distribution network. The simulation results show that the training time of neural network with preprocessed samples is shorter markedly.