By Topic

Time-series prediction using self-organizing fuzzy neural networks

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)
Ning Wang ; Sch. of Marine Eng., Dalian Maritime Univ., Dalian, China ; Xian-Yao Meng

A novel online self-constructing fuzzy neural network is proposed for time-series prediction. The proposed approach not only speeds up the learning process but also builds a more parsimonious fuzzy neural network while comparable performance and accuracy can be achieved since the new growing criteria feature characteristics of growing and pruning. The learning scheme starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growing criteria as learning proceeds. In the parameter learning phase, all free parameters of hidden units are updated by the extended Kalman filter (EKF) method. Simulation results demonstrate that the proposed approach can provide faster learning speed and more compact network structure with comparable generalization performance and accuracy.

Published in:

Information, Computing and Telecommunication, 2009. YC-ICT '09. IEEE Youth Conference on

Date of Conference:

20-21 Sept. 2009