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 multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.
|Title of host publication||Advances in Neural Information Processing Systems 7|
|Editors||G. Tesauro, D. S. Touretzky, T. D. Leen|
|Place of Publication||Dever, US|
|Number of pages||8|
|Publication status||Published - 28 Dec 1994|
|Event||Advances in Neural Information Processing Systems 1994 - Singapore, Singapore|
Duration: 16 Nov 1994 → 18 Nov 1994
|Other||Advances in Neural Information Processing Systems 1994|
|Period||16/11/94 → 18/11/94|
Bibliographical noteCopyright of the Massachusetts Institute of Technology Press (MIT Press)
- neural networks
- temperature plasmas
- magnetic fields
Bishop, C. M., Haynes, P. S., Smith, M. E. U., Todd, T. N., Trotman, D. L., & Windsor, C. G. (1994). Real-time control of a Tokamak plasma using neural networks. In G. Tesauro, D. S. Touretzky, & T. D. Leen (Eds.), Advances in Neural Information Processing Systems 7 (Vol. 7, pp. 1007-1014). MIT.