title: Development of a neural network technique for KSTAR Thomson scattering diagnostics.
abstract: 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 χ2method 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 χ2method. The best results were obtained for 103training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ2method and performs the calculation twenty times faster.
authors.affiliation: National Fusion Research Institute, 169-148 Gwahak-ro, Yuseong-gu, Daejeon 34133, South Korea.
authors.forename: Seung Hun
authors.initials: SH
authors.lastname: Lee
authors.affiliation: National Fusion Research Institute, 169-148 Gwahak-ro, Yuseong-gu, Daejeon 34133, South Korea.
authors.forename: J H
authors.initials: JH
authors.lastname: Lee
authors.affiliation: National Institute Fusion Science, Toki, Gifu 509-5292, Japan.
authors.forename: I
authors.initials: I
authors.lastname: Yamada
authors.affiliation: Department of Physics, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, South Korea.
authors.forename: Jae Sun
authors.initials: JS
authors.lastname: Park