In this paper, different pattern recognition techniques have been tested in order to implement an automatic tool for disruption classification in a tokamak experiment. The methods considered refer to clustering and classification techniques. In particular, the investigated clustering techniques are self-organizing maps and K-means, while the classification techniques are multi-layer perceptrons, support vector machines, and k- nearest neighbours. Training and testing data have been collected selecting suitable diagnostic signals recorded over 4 years of EFDA-JET experiments. Multi-layer perceptron classifiers exhibited the best performance in classifying mode lock, density limit/high radiated power, H-mode/L-mode transition and internal transport barrier plasma disruptions. This classification performance can be increased using multiple classifiers. In particular the outputs of five multi-layer perceptron classifiers have been combined using multiple classifier techniques in order to obtain a more robust and reliable classification tool, that is presently implemented at JET. © 2006 IAEA.
All Science Journal Classification (ASJC) codes
- Nuclear and High Energy Physics
- Condensed Matter Physics
Cannas, B., Cau, F., Fanni, A., Sonato, P., & Zedda, M. K. (2006). Automatic disruption classification at JET: Comparison of different pattern recognition techniques. Nuclear Fusion, 46(7), 699 - 708. . https://doi.org/10.1088/0029-5515/46/7/002