By Topic

A new method of rough RBF neural network ensembles

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
$33 $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

3 Author(s)
Xiao Di ; College of Automation Engineering, Nanjing Univiersity of Technology, 210009, China ; Lin Jinguo ; Hu Shousong

The performance of a single neural network is limited, but multiple neural networks can achieve higher classification accuracy and efficiency than the original single classifiers. In the paper, a new method of neural network ensembles based on rough set theory is described. An extended rough set model based real-value attribute is proposed, which decides the uncertainty problem of clustering regions for RBF hidden layer units. From the rough set theory, two cluster centers, which are lower and upper approximation cluster centers, can be required. Then, under the Experience Risk Minimum criterion, the two RBF neural networks with different hidden layer units could be combined. In the end of the paper, a simulation of flight actuators fault diagnosis is given, and results show that the method is valid and effective.

Published in:

2008 27th Chinese Control Conference

Date of Conference:

16-18 July 2008