Active charge exchange spectra representing the local interaction of injected neutral beams and fully stripped impurity ions are hard to analyse due to strong blending with passive emission from the plasma edge. As a result, the deduced plasma parameters (e.g. ion temperature, rotation velocity, impurity density) cannot always be determined unambiguously. Also, the speed of the analysis is limited by the time consuming nonlinear least-squares minimization procedure. In practice, semi-manual analysis is necessary and fast, automatic analysis, based on currently used techniques, does not seem feasible. In this paper the development of a robust and accurate analysis procedure based on multi-layer perceptron (MLP) neural networks is described. This procedure is fully automatic and fast, thus enabling a real-time analysis of charge exchange spectra. Accuracy has been increased in several ways as compared to earlier straightforward neural network implementations and is comparable to a standard least-squares based analysis. Robustness is achieved by using a combination of different confidence measures. A novel technique for the creation of training data, suitable for high-dimensional inverse problems has been developed and used extensively. A new method for fast calculation of error bars directly from the hidden neurons in a MLP network is also described, and used as part of the confidence calculations. For demonstration purposes, a real-time ion temperature profile diagnostic based on this work has been implemented.
Performance prediction of radio frequency based negative ion source using fusion neural network model