Abstract
This paper presents results from the first use of neural networks
for the real-time feedback control of high temperature plasmas in
a tokamak fusion experiment. The tokamak is currently the principal
experimental device for research into the magnetic confinement
approach to controlled fusion. In the tokamak, hydrogen
plasmas, at temperatures of up to 100 Million K, are confined
by strong magnetic fields. Accurate control of the position and
shape of the plasma boundary requires real-time feedback control
of the magnetic field structure on a time-scale of a few tens of microseconds.
Software simulations have demonstrated that a neural
network approach can give significantly better performance than
the linear technique currently used on most tokamak experiments.
The practical application of the neural network approach requires
high-speed hardware, for which a fully parallel implementation of
the multilayer perceptron, using a hybrid of digital and analogue
technology, has been developed.
Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems 7 (NIPS 1994) |
Publisher | MIT Press |
Pages | 1007-1014 |
Number of pages | 8 |
Volume | 7 |
Publication status | Published - 1994 |