This paper describes a pattern recognition method for off-line estimation of both L/H and H/L transition times in JET. The technique is based on a combined classifier to identify the confinement regime (L or H) at any time instant during a discharge. The classifier is a combination of two different classification systems: a Bayesian classifier whose likelihood is computed by means of a non-parametric statistical classifier (Parzen window) and a support vector machine classifier. They are combined through a fuzzy aggregation operator, in particular the Einstein sum. The success rate achieved exceeds 99% for the L to H transition and 96% for the H to L transition. The estimation of transition times is accomplished by following the temporal evolution of the confinement regimes. © 2009 IAEA, Vienna.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Nuclear and High Energy Physics
Vega, J., Murari, A., Vagliasindi, G., & Ratt, G. A. (2009). Automated estimation of L/H transition times at JET by combining Bayesian statistics and support vector machines. Nuclear Fusion, 49(8), -. . https://doi.org/10.1088/0029-5515/49/8/085023