By Topic

A method to resolve the overfitting problem in recurrent neural networks for prediction of complex systems’ behavior

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

4 Author(s)
Kaveh Mahdaviani ; Isfahan University of Technology and Isfahan Mathematics House, Iran ; Helga Mazyar ; Saeed Majidi ; Mohammad H. Saraee

In this paper a new method to resolve the overfitting problem for predicting complex systemspsila behavior has been proposed. This problem occurs when a neural network loses its generalization. The method is based on the training of recurrent neural networks and using simulated annealing for the optimization of their generalization. The major work is done based on the idea of ensemble neural networks. Finally the results of using this method on two sample datasets are presented and the effectiveness of this method is illustrated.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008