Neural network techniques offer a wide range of new opportunities for the analysis of data from plasma diagnostics. In particular, the class of neural network known as the multi-layer perceptron provides a general purpose approach to nonlinear data transformation between multidimensional spaces. In this paper, we outline the principles of the multi-layer perceptron, and illustrate its application to plasma diagnostics using two examples. The first of these concerns the extraction of line shape parameters from spectral data, and offers considerable improvements in speed compared with conventional approaches. The second application involves deconvolution of line integral data from a multichannel interferometer allowing the extraction of more detailed density provides than obtained by conventional Abel inversion.
|Number of pages||3|
|Journal||Review of Scientific Instruments|
|Publication status||Published - 1 Jan 1992|