A Normalization Method Based on Rough Set Theory
Chu Jian · 2009
The training time of classifier based on neural network is very long using the conventional normalization when the distances between samples of different classes are too small. To overcome the disadvantage, the normalization method based on rough set theory is proposed. By normalizing samples using rough ser theory, the samples which are near but belong to different classes are taken apart. The normalized samples are used to train neural network. The method is applied into neural network based fault line detection for distribution network. The simulation results show that the training time of neural network with processed samples is shorter markedly.