Open Science Research Excellence
@article{(International Science Index):,
  title    = {Artificial Neural Networks and Multi-Class Support Vector Machines for Classifying Magnetic Measurements in Tokamak Reactors},
  author    = {A. Greco and  N. Mammone and  F.C. Morabito and  M.Versaci},
  country   = {},
  abstract  = {This paper is mainly concerned with the application of
a novel technique of data interpretation for classifying measurements
of plasma columns in Tokamak reactors for nuclear fusion
applications. The proposed method exploits several concepts derived
from soft computing theory. In particular, Artificial Neural Networks
and Multi-Class Support Vector Machines have been exploited to
classify magnetic variables useful to determine shape and position of
the plasma with a reduced computational complexity. The proposed
technique is used to analyze simulated databases of plasma equilibria
based on ITER geometry configuration. As well as demonstrating the
successful recovery of scalar equilibrium parameters, we show that
the technique can yield practical advantages compared with earlier
    journal   = {International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering},  volume    = {2},
  number    = {7},
  year      = {2008},
  pages     = {1509 - 1516},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 19, 2008},