By Topic

Real-time extraction of plasma equilibrium parameters in KSTAR tokamak using statistical methods

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 $31
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

4 Author(s)
Yong-Su Na ; Department of Nuclear Engineering, Seoul National University, Seoul 151-742, Korea ; Jeon, Young-Mu ; Hong, S.H. ; Hwang, Y.S.

Your organization might have access to this article on the publisher's site. To check, click on this link: 

To improve inherent shortcomings of statistical methods and apply them to the extraction of plasma equilibrium parameters in a fast timescale for real-time plasma control, new concepts of statistical methods such as principal component analysis-based neural network (NN), functional parametrization (FP)-based NN and double network are introduced by modifying NN and FP. These new methods are benchmarked and compared with the conventional techniques of NN and FP in a simple single-filament system. As a result of their applications to identification of plasma equilibrium parameters in the Korea Superconducting Tokamak Advanced Research tokamak, particularly, the double network concept among them has successfully achieved the improvement of drawbacks in the conventional methods. It is shown that more reliable results from the double network method can be obtained by combining several different statistical treatments as a primary network. Even in the case of nonoptimized methods united as a primary network, quite acceptable results can be achieved in the double network method. © 2001 American Institute of Physics.

Published in:

Review of Scientific Instruments  (Volume:72 ,  Issue: 2 )