By Topic

Bi-directionalization of neural computing architecture for time series prediction. III. Application to laser intensity time record “Data Set A”

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

2 Author(s)
H. Wakuya ; Dept. of Adv. Syst. Control Eng., Saga Univ., Japan ; K. Shida

For part II, see Int. Conf. on Dynamical Aspects in Complex Systems from Cells to Brain, p.43-4 (2000). One of the most important targets of time series prediction is an improvement of prediction quality for aiming at prefect prediction. To reach the goal, most studies have used uni-directional computation flow to predict future events from present and past information. In this study, on the contrary, bi-directional computation style is applied to a time series prediction task to investigate its effectiveness. As a result of computer simulations with the laser intensity time record “Data Set A”, it is clear that the coupling effect between the future and past prediction transformations produce a good advantage on trainability, generalization, and prediction quality over the conventional uni-directional network

Published in:

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on  (Volume:3 )

Date of Conference:

2001