By Topic

Modeling and verification of the "cause &effect" relation comprehension by neural networks using learning 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
$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)
Muquit, M.A. ; Graduate Sch. of Inf. Sci., Tohoku Univ., Sendai, Japan ; Sawada, Yasuji

The capability of predicting what will happen next based on past experiences and environment features could be thought of as one kind of cause & effect comprehension. We have modeled a system to attain this capability by neural networks using reinforcement and supervised learning algorithms to comprehend the ground shot in a golf game. The system predicts the approximate distance the golf ball runs and we have found that the system works very well and it becomes almost perfect just after practicing with several samples

Published in:

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

Date of Conference:

2002