A technique using a single hidden layer backpropagation neural network is described to establish a nonlinear mapping between a set of magnetic flux measurements and some shaping parameters of a noncircular plasma. The technique has been applied for the identification of limiter and X point equilibria in the ASDEX Upgrade geometry; the dataset of equilibria required for training and testing the neural network has been generated by means of an integrated use of a fixed and a free boundary MHD code. The average accuracy of the identification procedure is quite good, with a further improvement if a linear connection between the input and output layers is introduced. A procedure is also proposed for the selection of the optimum location of a limited number of sensors. The relationship existing between the behaviour of the neural network and some statistical parameters of the dataset is analysed and discussed
Neural net modeling of equilibria in NSTX-U