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A Neural Network Inversion Technique for Plasma Interferometry in Toroidal Fusion Devices

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.

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A Neural Network Inversion Technique for Plasma Interferometry in Toroidal Fusion Devices

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  1. A Neural Network Inversion Technique for Plasma Interferometry in Toroidal FusionDevices Jerahmie Radder ECE 539 May 10, 2000

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

  3. 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 Problem of Chord Integral Measurement Inversion * Hyeon K. Park, Plasma Phys. Controlled Fusion 31, 2035 (1989).

  4. 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 RBF Network Strategy to Solve Inversion Problems bias y1 x1 x9 y5 Input Layer Hidden Layer (RBF) Output Layer (Linear)

  5. Initial RBF Inversion Results • Circular Geometry • Concentric Inversion Regions • 5% Noise Plasma Profile Integral Meas.

  6. 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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