Neural network of Gaussian radial basis functions applied to the problem of identification of nuclear accidents in a PWR nuclear power plant
Tài liệu tham khảo
Alvarenga, M.A.B., 1997. Diagnóstico do desligamento de um reator nuclear através de técnicas avançadas de inteligência artificial. Tese de Doutorado, COPPE/UFRJ, Rio de Janeiro, Brasil.
Bartal, Y., Lin, J., Uhrig, R.E., 1994. Nuclear power plants transient diagnostics using LVQ or Some networks don’t know that they don’t know. In: Proc. IEEE International Conference on Neural Networks, USA.
Bartlett, 1992, Nuclear power plant status diagnostics using an artificial neural network, Nucl. Technol., 97, 272, 10.13182/NT92-A34635
Basu, A., 1992. Nuclear power plant status diagnostics using a neural network with dynamic node architecture. Iowa State University. MS thesis.
Benedek, S., Embrechts, M.J., 1996. Rapid identification of nuclear power plant malfunctions with artificial neural networks via Fourier transformed signals. In: Proc. ANNNIE’96, St. Louis, MO.
Elman, 1990, Finding structure in time, Cogn. Sci., 14, 179, 10.1207/s15516709cog1402_1
Furukawa, H., Ueda, T., Kitamura, M., 1995. Use of self-organizing neural networks for rational definition of plant diagnostic symptoms. In: Proc. International Topical Meeting on Computer-Based Human Support Systems, Philadelphia.
Glossário de Segurança Nuclear da CNEN, 2012. Comissão Nacional de Energia Nuclear. <http://www.cnen.gov.br/seguranca/normas/pdf/glossario.pdf>.
Guo, Z., Uhrig, R.E., 1992. Using modular neural networks to monitor accident conditions in nuclear power plants. In: Proceedings of the SPIE Technical Symposium on Intelligent Information Systems, Application of Artificial Neural Networks III, Orlando, USA.
Haykin, 2001
Medeiros, J.A.C.C., 2005. Enxames de partículas como ferramenta de otimização em problemas complexos da engenharia nuclear. Tese de D.Sc., COPPE/UFRJ, Rio de Janeiro, RJ, Brasil.
Mol, A.C.A., 2002. Um sistema de identificação de transientes com inclusão de ruídos e Indicação de eventos desconhecidos, Tese de D. Sc., COPPE/UFRJ, Rio de Janeiro, RJ, Brasil.
Mol, 2003, A neural model for transient identification in dynamic processes with “do not know” response, Ann. Nucl. Energy, 30, 1365, 10.1016/S0306-4549(03)00072-0
Mol, 2006, Neural and genetic-based approaches to nuclear transient identification including “don’t know” response, Prog. Nucl. Energy, 48, 268, 10.1016/j.pnucene.2005.07.002
Moshkbar-Bakhshayesh, 2013, Transient identification in nuclear power plants: a review, Prog. Nucl. Energy, 67, 23, 10.1016/j.pnucene.2013.03.017
Na, 2004, Prediction of major transient scenarios for severe accidents of nuclear power plants, IEEE Trans. Nucl. Sci., 51, 313, 10.1109/TNS.2004.825090
Park, 1991, Universal approximation using radial basis function networks, Neural Comput., 3, 246, 10.1162/neco.1991.3.2.246
Park, 1993, Approximation and radial-basis function networks, Neural Comput., 5, 305, 10.1162/neco.1993.5.2.305
Powell, 1987, Radial basis functions for multivariable interpolation: a review, 143