Stochastic regularization of feedwater flow rate evaluation for the venturi meter fouling problem in nuclear power plants

Inverse Problems in Engineering

Volumn 9, Issue 6, 2001, Pages 671-696

  Gribok, Andrei V ^a   Attieh, Ibrahim K ^a   Hines, J Wesley ^a   Uhrig, Robert E ^a  

^a University of Tennessee   (United States)

Author keywords

Fouling; Inferential sensing; Least squares solution; Regularization; Ridge regression; Venturi meter

Indexed keywords

EID: 0346407923     PISSN: 10682767     EISSN: None     Source Type: Journal    
DOI: 10.1080/174159701088027786     Document Type: Article

References (19)

1. A.M.C. Chan, A.K. Ahluwalia (1992). Feedwater flow measurement in U.S. nuclear power generation stations, EPRI TR-101388, Electric Power Research Institute, Nov.
2. K. Kavaklioglu, and Upadhyaya, Belle R. (1994). Monitoring feedwater flow rate and component thermal performance of pressurized water reactors by means of artificial neural networks. Nuclear Technology, 107, July, pp. 112-123.
3. J.W. Hines, Andrei V. Gribok, Ibrahim Attieh and Robert E. Uhrig (1999). Regularization methods for inferential sensing in nuclear power plants. In: Da Ruan (Ed.), Fuzzy Systems and Soft Computing in Nuclear Engineering, Springer.
4. J. Hadamard (1923). Lectures on Cauchy's Problem in Linear Partial Differential Equations, Yale University Press, New Haven.
5. D.A. Belsley, E. Kuh, R.E. Welsch (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, John Wiley and Sons, New York.
6. B. Efron (1982). The Jacknife, The Bootstrap, and Other Resampling Plans, Philadelphia, Penn.: Society for Industrial and Applied Mathematics.
7. G. Strang (1993). Introduction to linear algebra. Wellesley-Cambridge Press. Wellesley MA.
8. P.C. Hansen (1997). Rank-deficient and discrete Ill-posed problems. Numerical Aspects of Linear Inversion, SIAM, Philadelphia.
9. P.C. Hansen (1989). Regularization, GSVD and truncated GSVD, BIT 29 pp. 491-504.
10. A.E. Hoerl and R.W. Kennard (1970). Ridge regression: applications to non-orthogonal problems. Technometrics, 12(1), 69-82
11. A.E. Hoerl, R.W. Kennard (1970). Ridge regression: biased estimation for non-orthogonal problems. Technometrics, 12, 55-67.
12. C.M. Theobald (1974). Generalization of mean square error applied to ridge regression, Journal of the Royal Statistical Society, Ser.B, 36, 106-106.
13. V.A. Morozov (1966). On the solution of functional equations by the method of regularization, Soviet Math. Dokl., 7, 414-417.
14. L. Louis Scharf (1999). The SVD and reduced-rank signal processing. In: R. Vaccaro (Ed.), SVD and Signal Processing II: Algorithms, Applications and Architectures, pp. 3-31. Elsevier Science Publishers, North Holland.
15. G.H. Golub, M. Heath, G. Wahba (1979). Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, 21(2), 215-223.
16. P.C. Hansen (1992). Analysis of discrete ill-posed problems by means of the L-curve, SIAM Review, 34(4), 561-580.
17. A.B. Bakushinskii (1984). Remarks on choosing a regularization parameter using the quasi-optimality and ratio criterion, USSR Comput. Math. and Math. Phys., 24(4), 181-182.
18. A.R. Davies, M.F. Hassan (1987). Optimality in the regularization of ill-posed problems, In: P.C. Sabatier (Ed.), Inverse Problems: An Interdisciplinary Study, pp. 553-562. Academic Press Limited, London.
19. P.C. Hansen (1994). Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems, Numer. Algorithms, 6, 1-35.