Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Online speed control of permanent-magnet synchronous motor using self-constructing recurrent fuzzy neural network

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)
Hung-Ching Lu ; Dept. of Electr. Eng., Tatung Univ., Taipei ; Ming-Hung Chang

In this paper, a self-constructing recurrent fuzzy neural network (SCRFNN) method is proposed to control the speed of a permanent-magnet synchronous motor to track periodic reference trajectories. The proposed SCRFNN combines the merits of self-constructing fuzzy neural network (SCFNN) and the recurrent neural network (RNN). The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient-decent method. In addition, the Mahalanobis distance (M-distance) formula is employed that the neural network has the ability of identification of the neurons will be generated or not. Finally, the simulated results show that the control effort is robust.

Published in:

Machine Learning and Cybernetics, 2008 International Conference on  (Volume:7 )

Date of Conference:

12-15 July 2008