A Comparison between Soft Computing and Statistic Approaches to Identify Plasma Columns in Tokamak Reactors

Author

Calcagno, Salvatore ; Greco, Antonino ; Morabito, Francesco Carlo ; Versaci, Mario

Author_Institution

Univ. "Mediterranea" degli Studi, Reggio Calabria

fYear

0

fDate

0-0 0

Firstpage

835

Lastpage

842

Abstract

This paper is concerned with the application of novel techniques of data interpretation for reconstructing plasma shape in Tokamak reactors for nuclear fusion applications. In particular, Artificial Neural Networks have been taken into account to estimate the distance of the plasma boundary from the fist wall of the vacuum vessel in ITER configuration. In addition, a comparison with Principal Component Analysis and Functional Parameterization is presented. Finally, in order to reduce the computational complexity, non linear techniques for ranking sensors is exploited.

Keywords

Tokamak devices; neural nets; nuclear engineering computing; physics computing; principal component analysis; Functional Parameterization; ITER configuration; Principal Component Analysis; Tokamak reactors; artificial neural networks; computational complexity; data interpretation; non linear techniques; nuclear fusion; plasma columns; plasma shape reconstructing; sensors; soft computing; statistic approaches; Fusion reactors; Inductors; Informatics; Magnetic flux; Plasma applications; Plasma measurements; Shape; Statistics; Tokamaks; Transportation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9490-9

Type

conf

DOI

10.1109/IJCNN.2006.246771

Filename

1716182