A Neural Network Inversion Technique for Plasma Interferometry in Toroidal Fusion Devices Jerahmie Radder ECE 539 May 10, 2000

Outline Problem of Chord Integral Measurement Inversion RBF Network Strategy to Solve Inversion Problems Initial Results Conclusions and Future Work

Problem of Chord Integral Measurement Inversion Measurements Are Line of Sight Integrals All Detectors are Placed Outside the Plasma Boundary Ill-Posed Problem: Multiple Solutions May Exist for Each Profile Geometry of Poloidal Cross-Section Must be Known Inverted Quantities are Assumed to be Constant on Magnetic Surfaces * Hyeon K. Park, Plasma Phys. Controlled Fusion 31, 2035 (1989).

RBF Network Strategy to Solve Inversion Problems Network Type: Radial Basis Function Network 9 Input Nodes: 9-Chord Interferometer Hidden Layer: Chosen to Minimize the Sum of Squares Error Function 5 Outputs: 5 Plasma Regions x1x1 x9x9 bias y1y1 y5y5 Input Layer Hidden Layer (RBF) Output Layer (Linear)

Initial RBF Inversion Results Circular Geometry Concentric Inversion Regions 5% Noise Plasma ProfileIntegral Meas.

Conclusions / Future Work RBF Networks Provide a Useful Tool for Reconstruction of Spatial Distributions from Chord Integral Measurements Initial Results Using Simple Geometries Produce Promising Results Further Efforts Include: –Comparison to Conventional Inversion Techniques –Implementation for Complicated Geometries (HSX, etc.)