A fuzzy-neural approach to real time plasma boundary reconstruction in tokamak reactors

A novel plasma shape identification procedure based on both neural network (NN) and fuzzy system (FS) approaches is presented. The derived processor takes firstly advantage of the fuzzy curves (FCs) concept for carrying out a guided dimensionality reduction of the available pattern of measurements (inputs of the reconstruction procedure). The mapping between the input pattern and the corresponding set of parameters describing the plasma (outputs of the procedure) is then approximated by a FS whose bank of rules is directly extracted from the relevant FCs. An extremely simplified NN is finally trained to learn the estimation error of the above mentioned fuzzy block. One of the most relevant consequences of the analysis carried out on the first phase is the possibility of using a very limited number of measurements for correcting the mapping. This may have an impact on both the rapidity of the identification in real time and the reduction of the number of sensors required to achieve a prescribed accuracy for control. As a by-product, the designed fuzzy-neural system reduces the extrapolation problems typically encountered by the researchers when using simple NN models