Table of Contents
Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 374816, 8 pages
Research Article

Multilayer Perceptron for Prediction of 2006 World Cup Football Game

Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu 30010, Taiwan

Received 9 May 2011; Revised 9 September 2011; Accepted 23 September 2011

Academic Editor: Mohamed A. Zohdy

Copyright © 2011 Kou-Yuan Huang and Kai-Ju Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multilayer perceptron (MLP) with back-propagation learning rule is adopted to predict the winning rates of two teams according to their official statistical data of 2006 World Cup Football Game at the previous stages. There are training samples from three classes: win, draw, and loss. At the new stage, new training samples are selected from the previous stages and are added to the training samples, then we retrain the neural network. It is a type of on-line learning. The 8 features are selected with ad hoc choice. We use the theorem of Mirchandani and Cao to determine the number of hidden nodes. And after the testing in the learning convergence, the MLP is determined as 8-2-3 model. The learning rate and momentum coefficient are determined in the cross-learning. The prediction accuracy achieves 75% if the draw games are excluded.