P. Baraldi, R. Razavi-far, and E. Zio, Bagged Ensemble of FCM Classifier for Nuclear Transient Identification, 2010.

L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

L. Breiman, Combining predictors, in Combining Artificial Neural Nets, 1999.

J. Chen and R. J. Patton, Robust model-based fault diagnosis for dynamic systems Asian studies in computer science and information science, 1999.

M. J. Embrechts and S. Benedek, Hybrid Identification of Nuclear Power Plant Transients With Artificial Neural Networks, IEEE Transactions on Industrial Electronics, vol.51, issue.3, pp.686-693, 2004.
DOI : 10.1109/TIE.2004.824874

A. Evsukoff and S. Gentil, Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors, Advanced Engineering Informatics, vol.19, issue.1, pp.55-66, 2005.
DOI : 10.1016/j.aei.2005.01.009

L. K. Hansen and P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.10, pp.993-1001, 1990.
DOI : 10.1109/34.58871
URL : http://orbit.dtu.dk/en/publications/neural-network-ensembles(492f6c68-703a-4b6d-97bb-8509d817d00f).html

J. W. Hines, D. W. Miller, and B. K. Hajek, A hybrid approach for detecting and isolating faults in nuclear power plant interacting systems, Nuclear Technology, vol.115, issue.3, 1996.

L. I. Kuncheva, Classifier Ensembles for Changing Environments. Book series lecture notes in computer science, pp.1-15, 2004.
DOI : 10.1007/978-3-540-25966-4_1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.99.388

M. G. Na, S. H. Shin, S. M. Lee, D. W. Jung, S. P. Kim et al., Prediction of Major Transient Scenarios for Severe Accidents of Nuclear Power Plants, IEEE Transactions on Nuclear Science, vol.51, issue.2, pp.313-321, 2004.
DOI : 10.1109/TNS.2004.825090

B. Parhami, Voting algorithms, IEEE Transactions on Reliability, vol.43, issue.4, pp.617-629, 1994.
DOI : 10.1109/24.370218

R. Polikar, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, vol.6, issue.3, pp.21-45, 2006.
DOI : 10.1109/MCAS.2006.1688199

R. Polikar, L. Udpa, S. S. Udpa, and V. Honavar, Learn++: an incremental learning algorithm for supervised neural networks, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.31, issue.4, 2001.
DOI : 10.1109/5326.983933

E. Puska and S. Normann, 3-d core studies for hambo simulator In proceedings of presentations on man-machine system research, Enlarged Halden programme group meeting, 2002.

J. Reifman, Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants, Nuclear Technology, vol.119, pp.76-97, 1997.

R. Razavi-far, H. Davilu, V. Palade, and C. Lucas, Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks, Neurocomputing, vol.72, issue.13-15, pp.72-2939, 2009.
DOI : 10.1016/j.neucom.2009.04.004

D. Roverso, On-line early fault detection and diagnosis with the alladin transient classifier. Proceedings of PNPIC and HMIT-2004, the 4 th American Nuclear Society, International Topical Meeting on Nuclear Plant Instrumentation Control and Human-Machine Interface Technologies, 2004.

S. Simani, C. Fantuzzi, and R. J. Patton, Model-based fault diagnosis in dynamic systems using identification techniques, 2002.
DOI : 10.1007/978-1-4471-3829-7

R. E. Uhrig, Soft computing technologies in nuclear engineering applications, Progress in Nuclear Energy, vol.34, issue.1, pp.13-75, 1999.
DOI : 10.1016/S0149-1970(97)00109-1

L. Xu, A. Krzyzak, and C. Y. Suen, Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.3, pp.418-435, 1992.
DOI : 10.1109/21.155943

B. Yuan and G. Klir, Intelligent Hybrid Systems Fuzzy Logic, Neural Network, and Genetic Algorithms, 1997.

K. Zhao and B. R. Upadhyaya, Adaptive fuzzy inference causal graph approach to fault detection and isolation of field devices in nuclear power plants, Progress in Nuclear Energy, pp.3-4, 2005.
DOI : 10.1016/j.pnucene.2005.03.006

E. Zio and P. Baraldi, Identification of nuclear transients via optimized fuzzy clustering, Annals of Nuclear Energy, vol.32, issue.10, pp.1068-1080, 2005.
DOI : 10.1016/j.anucene.2005.02.012

E. Zio, P. Baraldi, and G. Gola, Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery, Applied Soft Computing, vol.8, issue.4, pp.1365-1380, 2008.
DOI : 10.1016/j.asoc.2007.10.005

E. Zio, P. Baraldi, and N. Pedroni, Selecting features for nuclear transients classification by means of genetic algorithms, IEEE Transactions on Nuclear Science, vol.53, issue.3, pp.1479-1493, 2006.
DOI : 10.1109/TNS.2006.873868