Confinement Regime Identification in Nuclear Fusion via an Interpretable Fuzzy Logic Classifier

In this paper a data driven methodology to automatically derive an interpretable Fuzzy Logic Classifier (FLC) has been applied to the problem of confinement regime identification in the Joint European Torus. The approach has been developed explicitly to handle with the complexities of the inference process in Magnetic Confinement Nuclear Fusion (MCNF). The first step of the method consists of a supervised, exploratory analysis performed with the approach of Classification and Regression Trees (CART), to extract the variables in the database which are the most critical for the problem under study. Then, a fully automated algorithm determines the membership functions and the most appropriate rules to reproduce the classification tree obtained with CART. The resulting FLI on the one hand attains very good performance in terms of generalization and classification, on the other hand provides a series of rules which can be easily interpreted and contributing to a very good first, intuitive understanding of the physics involved.