By Topic

Optimal feed-forward neural networks based on the combination of constructing and pruning by genetic algorithms

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

6 Author(s)
Wenjian Wang ; Inst. for Inf. & Syst. Sci., Xi'an Jiaotong Univ., China ; Weizhen Lu ; A. Y. T. Leung ; Siu-Ming Lo
more authors

The determination of the proper size of an artificial neural network (ANN) is recognized to be crucial, especially for its practical implementation in important issues such as learning and generalization. In the paper, an effective design method of neural network architectures is presented. The network is firstly trained by a dynamic constructive method until the error is satisfied. The trained network is then pruned by genetic algorithm (GA). The simulation results demonstrate the advantages in generalization and expandability of the proposed method

Published in:

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on  (Volume:1 )

Date of Conference:

2002