Development of a neural network technique for KSTAR Thomson scattering diagnostics
- National Fusion Research Institute, 169-148 Gwahak-ro, Yuseong-gu, Daejeon 34133 (Korea, Republic of)
- National Institute Fusion Science, Toki, Gifu 509-5292 (Japan)
- Department of Physics, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141 (Korea, Republic of)
Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ{sup 2} method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ{sup 2} method. The best results were obtained for 10{sup 3} training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ{sup 2} method and performs the calculation twenty times faster.
- OSTI ID:
- 22596469
- Journal Information:
- Review of Scientific Instruments, Vol. 87, Issue 11; Other Information: (c) 2016 Author(s); Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6748
- Country of Publication:
- United States
- Language:
- English
Similar Records
DIII-D Thomson Scattering Diagnostic Data Acquisition, Processing and Analysis Software
Identification of Plasma Parameters and Optimization of Magnetic Sensors in the Superconducting Steady-State Tokamak-1 Using Neural Networks