Radial basis function networks applied to DNBR calculation in digital core protection systems

Annals of Nuclear Energy

Volumn 30, Issue 15, 2003, Pages 1561-1572

  Lee, Gyu Cheon ^a   Chang, Soon Heung ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ACCIDENT PREVENTION; NUCLEAR REACTOR ACCIDENTS; RADIAL BASIS FUNCTION NETWORKS; REACTOR SHUTDOWNS;

DIGITAL CORE PROTECTION;

NUCLEAR POWER PLANTS;

EID: 0038009289     PISSN: 03064549     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0306-4549(03)00099-9     Document Type: Article

References (9)

1. Chen S., Cowan C., Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Tran. on Neural Networks. 2(2):1991;302-309.
2. Chiu, Chong Three-dimensional lumped subchannel model and predition-correction numerical method for thermal margin analysis of PWR cores. Nuclear Engineering And Design. 64:1980;103-115.
3. Garis N.S., Pázsit I., Sandberg U., Andersson T. Determination of PWR control rod position by core physics and neural network methods. Nuclear Technology. 123:1998;278-295.
4. Kim H.G., Chang SoonHeung, Lee ByungHo Pressurized water reactor core parameter prediction using an artificial neural network. Nuclear Science and Engineering. 113:1993;70-76.
5. Moon S.K., Chang S.H. Classification and prediction of critical heat flux using fuzzy theory and artificial neural networks. Nuclear Engineering and Design. 150:1994;151-161.
6. Ober T.G., Storu G.H., Rickard I.C., Gasper J.K. Theory, capabilities, and use of the three-dimensional reactor operation and control simulator (ROCS). Nuclear Science and Engineering. 64:1977;605-623.
7. Poggio, T. and Girosi, F. 1989, A Theory of Networks for Approximation and Learning, A.I. Memo No.1140.
8. Powell M.J.D. Algorithms for Approximation. 1987;Oxford University Press, Oxford.
9. Sarle, W.S., ed. 1997, Neural Network FAQ. Available at: ftp://ftp.sas.com/pub/neural/FAQ.html .