Scheduled System Maintenance on May 29th, 2015:
IEEE Xplore will be upgraded between 11:00 AM and 10:00 PM EDT. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

Time Series Prediction Using Robust Radial Basis Function with Two-Stage Learning Rule

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)
Chien-Cheng Lee ; Yuan Ze Univ., Taoyuan ; Cheng-Yuan Shih

The radial basis function neural network (RBFNN) is a well known method for many kinds of application, including function approximation, classification, and prediction. However, the traditional RBFNN is not robust for the training data which contains outliers. In this paper, we propose a two-stage learning rule for RBFNN to eliminate the influence of outliers. The concept of the Chebyshev theorem for detecting outlier is adopted to filter out the potential outliers in the first stage, and the M-estimator is used for dealing with the insignificant outliers in the second stage. The experimental results show that the proposed method can reduce the prediction error compared with other methods. Furthermore, even though fifty percent of all observations are the outliers this method still has a good performance.

Published in:

Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on  (Volume:2 )

Date of Conference:

29-31 Oct. 2007