Geometrical Kernel Machine for Prediction and Novelty Detection of Disruptive Events in TOKAMAK Machines

This paper presents a so called Geometrical Kernel Machine used to predict disruptive events in nuclear fusion reactors. Here, the prediction problem is modeled as a two classes classification problem, and the predictor is built by using a new constructive algorithm that allows us to automatically determine both the number of neurons and the synaptic weights of a Multilayer Perceptron network with a single hidden layer. It has been demonstrated that the resulting network is able to classify any set of patterns defined in a real domain. The geometrical interpretation of the network equations allows us both to develop the predictor and to manage the so called ageing of the kernel machine. In fact, using the same kernel machine, a novelty detection system has been integrated in the predictor, increasing the overall system performance.