Prediction of golden time using SVR for recovering SIS under severe accidents
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[1] Man Gyun Na,et al. Detection and Diagnostics of Loss of Coolant Accidents Using Support Vector Machines , 2008, IEEE Transactions on Nuclear Science.
[2] Jong-Ho Park,et al. A Study on Severe Accident Management Scheme using LOCA Sequence Database System , 2014 .
[3] Maryam Abbasi,et al. Uncertainty analysis of support vector machine for online prediction of five-day biochemical oxygen demand , 2015 .
[4] Man Gyun Na,et al. Inferential Sensing and Monitoring for Feedwater Flowrate in Pressurized Water Reactors , 2006, IEEE Transactions on Nuclear Science.
[5] Xiaobing Kong,et al. Wind speed prediction using reduced support vector machines with feature selection , 2015, Neurocomputing.
[6] Melanie Mitchell,et al. An introduction to genetic algorithms , 1996 .
[7] Dimitri P. Bertsekas,et al. Constrained Optimization and Lagrange Multiplier Methods , 1982 .
[8] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[9] Man Gyun Na,et al. PREDICTION OF THE REACTOR VESSEL WATER LEVEL USING FUZZY NEURAL NETWORKS IN SEVERE ACCIDENT CIRCUMSTANCES OF NPPS , 2014 .
[10] David E. Goldberg,et al. Genetic Algorithms in Search Optimization and Machine Learning , 1988 .
[11] Man Gyun Na,et al. PREDICTION OF HYDROGEN CONCENTRATION IN CONTAINMENT DURING SEVERE ACCIDENTS USING FUZZY NEURAL NETWORK , 2015 .
[12] Man Gyun Na,et al. Smart Sensing of the RPV Water Level in NPP Severe Accidents Using a GMDH Algorithm , 2014, IEEE Transactions on Nuclear Science.
[13] Belle R. Upadhyaya,et al. Sensor monitoring using a fuzzy neural network with an automatic structure constructor , 2003 .