Cart (Loading....) | Create Account
Close category search window
 

Neural network ensembles and their application to traffic flow prediction in telecommunications networks

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

3 Author(s)
Xin Yao ; Sch. of Comput. Sci., Birmingham Univ., UK ; Fischer, M. ; Brown, G.

It is well-known that large neural networks with many unshared weights can be very difficult to train. A neural network ensemble consisting of a number of individual neural networks usually performs better than a complex monolithic neural network. One of the motivations behind neural network ensembles is the divide-and-conquer strategy, where a complex problem is decomposed into different components each of which is tackled by an individual neural network. A promising algorithm for training neural network ensembles is the negative correlation learning algorithm which penalizes positive correlations among individual networks by introducing a penalty term in the error function. A penalty coefficient is used to balance the minimization of the error and the minimization of the correlation. It is often very difficult to select an optimal penalty coefficient for a given problem because as yet there is no systematic method available for setting the parameter. This paper first applies negative correlation learning to the traffic flow prediction problem, and then proposes an evolutionary approach to deciding the penalty coefficient automatically in negative correlation learning. Experimental results on the traffic flow prediction problem will be presented

Published in:

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:1 )

Date of Conference:

2001

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.