Dynamic Clustering and Neuro-Fuzzy Identification for the Analysis of Fusion Plasma Signals

Abstract

Measurements in long pulse devices like ITER require the use of intelligent techniques to detect interesting events and anomalous behaviors within a continuous data flow. This detection will trigger the execution of some experimental procedures such as: increasing sampling rates, starting data sampling in additional channels or notifying the event to other diagnostics. In a first approach, an interesting event can be any non-average behavior in the expected temporal evolution of the waveforms. Therefore, a model of the signals is needed. In this work, a model that represents each type of plasma signal is obtained by means of fuzzy inference systems (FIS) which are generated by applying adaptive neuro-fuzzy techniques. The purpose of this neuro-fuzzy modeling is to identify patterns of these groups of data to produce a concise representation of a signal. Previously the signals have been preprocessed and a new dynamic clustering strategy based on a partitioning method has been applied to obtain the clusters. Off-line analyses have been applied to bolometric signals of the fusion device TJ-II Stellator with encouraging results.