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

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

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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).

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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)

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

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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.)

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