Applying neural networks to control the TFTR neural beam ion sources

Abstract

The author describes the application of neural networks to the control of the neural beam long-pulse positive ion source accelerators on the Tokamak Fusion Test Reactor (TFTR) at Princeton University. Neural networks were used to learn how the operators adjust the control setpoints when running these sources. The data sets used to train these networks were derived from a large database containing actual setpoints and power supply waveform calculations for the 1990 run period. The networks learned what the optimum control setpoints should initially be set based upon desired accel voltage and perveance levels. Neural networks were also used to predict the divergence of the ion beam.<>