Adaptive high learning rate probabilistic disruption predictors from scratch for the next generation of tokamaks
暂无分享,去创建一个
J. Vega | S. Dormido-Canto | A. Murari | R. Moreno | J. Contributors | A. Pereira | A. Acero | A. Murari | A. Murari | Jesús Vega | J. Vega
[1] David G. Stork,et al. Pattern Classification , 1973 .
[2] K. F. Mast,et al. Disruptions in JET , 1989 .
[3] Teuvo Kohonen,et al. The self-organizing map , 1990 .
[4] M. F. F. Nave,et al. Mode locking in tokamaks , 1990 .
[5] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[6] Paul M. Frank,et al. Fault Diagnosis in Dynamic Systems , 1993, Robotics, Mechatronics and Manufacturing Systems.
[7] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.
[8] Vladimir Cherkassky,et al. Learning from data , 1998 .
[9] Alexander Gammerman,et al. Machine-Learning Applications of Algorithmic Randomness , 1999, ICML.
[10] Alexander Gammerman,et al. Transduction with Confidence and Credibility , 1999, IJCAI.
[11] A. Sengupta,et al. Forecasting disruptions in the ADITYA tokamak using neural networks , 2000 .
[12] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[13] Alessandra Fanni,et al. Disruption forecasting at JET using neural networks , 2002 .
[14] Harry Wechsler,et al. Transductive confidence machine for active learning , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[15] Jet Efda Contributors,et al. A cross-tokamak neural network disruption predictor for the JET and ASDEX Upgrade tokamaks , 2005 .
[16] W. Gasarch,et al. The Book Review Column 1 Coverage Untyped Systems Simple Types Recursive Types Higher-order Systems General Impression 3 Organization, and Contents of the Book , 2022 .
[17] Piergiorgio Sonato,et al. A prediction tool for real-time application in the disruption protection system at JET , 2007 .
[18] J. Manickam,et al. Chapter 3: MHD stability, operational limits and disruptions , 2007 .
[19] Zhiyuan Luo,et al. Reliable Probabilistic Classification and Its Application to Internet Traffic , 2008, ICIC.
[20] Harry Wechsler,et al. Query by Transduction , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[21] Luigi Fortuna,et al. Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees , 2008 .
[22] P. Arena,et al. A Disruption Predictor Based on Fuzzy Logic Applied to JET Database , 2008, IEEE Transactions on Plasma Science.
[23] Sethuraman Panchanathan,et al. Generalized Query by Transduction for online active learning , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[24] G. Granucci,et al. Disruption control on FTU and ASDEX upgrade with ECRH , 2009 .
[25] J. Contributors,et al. Statistical analysis of disruptions in JET , 2009 .
[26] F. Saint-Laurent,et al. Experimental study of disruption mitigation using massive injection of noble gases on Tore Supra , 2010 .
[27] D. A. Humphreys,et al. Demonstration of rapid shutdown using large shattered deuterium pellet injection in DIII-D , 2010 .
[28] B. Cannas,et al. An adaptive real-time disruption predictor for ASDEX Upgrade , 2010 .
[29] Mohammad Bakhtiari,et al. Using mixed gases for massive gas injection disruption mitigation on Alcator C-Mod , 2010 .
[30] Andrea Murari,et al. An advanced disruption predictor for JET tested in a simulated real-time environment , 2010 .
[31] Alessandra Fanni,et al. Disruption prediction with adaptive neural networks for ASDEX Upgrade , 2011 .
[32] P. D. Morgan,et al. Disruption mitigation by massive gas injection in JET , 2011 .
[33] J. Contributors,et al. Survey of disruption causes at JET , 2011 .
[34] D. A. Humphreys,et al. Novel rapid shutdown strategies for runaway electron suppression in DIII-D , 2011 .
[35] G. Pautasso,et al. Prediction of disruptions on ASDEX Upgrade using discriminant analysis , 2011 .
[36] J. Vega,et al. Results of the JET real-time disruption predictor in the ITER-like wall campaigns , 2012 .
[37] Vladimir Vovk,et al. Multiprobabilistic Venn Predictors with Logistic Regression , 2012, AIAI.
[38] DIII-D Team,et al. The limits and challenges of error field correction for ITERa) , 2012 .
[39] Allen H. Boozer,et al. Theory of tokamak disruptionsa) , 2012 .
[40] R. H. Bulmer,et al. Sustained Spheromak Physics Experiment (SSPX): design and physics results , 2012 .
[41] Harris Papadopoulos,et al. Reliable Probability Estimates Based on Support Vector Machines for Large Multiclass Datasets , 2012, AIAI.
[42] R. Neu,et al. The impact of the ITER-like wall at JET on disruptions , 2012 .
[43] Jet Efda Contributors,et al. Development of an efficient real-time disruption predictor from scratch on JET and implications for ITER , 2013 .
[44] Alessandra Fanni,et al. Multivariate statistical models for disruption prediction at ASDEX Upgrade , 2013 .
[45] S. A. Sabbagh,et al. Detection of disruptions in the high-β spherical torus NSTX , 2013 .
[46] Harris Papadopoulos,et al. Reliable probabilistic classification with neural networks , 2013, Neurocomputing.