Title :
A Normalization Method Based on Rough Set Theory
Author_Institution :
Dept. of Autom. Eng., Tianjin Univ. of Technol. & Educ., Tianjin
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; rough set theory; classifier; neural network; normalization method; rough set theory; Automation; Backpropagation algorithms; Educational technology; Fault detection; Feedforward neural networks; Information systems; Neural networks; Object detection; Set theory; Uncertainty;
Conference_Titel :
Intelligent Systems and Applications, 2009. ISA 2009. International Workshop on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3893-8
Electronic_ISBN :
978-1-4244-3894-5
DOI :
10.1109/IWISA.2009.5072653
