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

To improve inherent shortcomings of statistical methods and apply them to t he 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. Thes e 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 appl ications to identification of plasma equilibrium parameters in the Korea Su perconducting Tokamak Advanced Research tokamak, particularly, the double n etwork concept among them has successfully achieved the improvement of draw backs in the conventional methods. It is shown that more reliable results f rom the double network method can be obtained by combining several differen t statistical treatments as a primary network. Even in the case of nonoptim ized methods united as a primary network, quite acceptable results can be a chieved in the double network method. (C) 2001 American Institute of Physic s.