By Topic

Normalization method based on rough set theory and application in fault line detection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Qingle Pang ; Sch. of Inf. & Electron. Eng., Shandong Inst. of Bus. & Technol., Yantai ; Qian Xu

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.

Published in:

Control and Decision Conference, 2008. CCDC 2008. Chinese

Date of Conference:

2-4 July 2008