Heuristic, systematic, and informational regularization for process monitoring

International Journal of Intelligent Systems

Volumn 17, Issue 8, 2002, Pages 723-749

  Gribok, Andrei V ^a   Hines, J Wesley ^a   Urmanov, Aleksey ^a   Uhrig, Robert E ^a  

^a University of Tennessee Knoxville   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CALIBRATION; FEEDWATER ANALYSIS; HEURISTIC METHODS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; NUCLEAR POWER PLANTS; PROCESS CONTROL; PROCESS ENGINEERING; REGRESSION ANALYSIS; SENSORS;

NUCLEAR POWER PLANT FEEDWATER FLOW; PROCESS MONITORING; REGULARIZATION METHODS;

NEURAL NETWORKS;

EID: 0036671802     PISSN: 08848173     EISSN: None     Source Type: Journal    
DOI: 10.1002/int.10047     Document Type: Article

References (50)

1. Information theory and an extension of the maximum likelihood principle
2. ICOMP: A new model selection criterion
3. Informational complexity criteria for regression models
4. Generalized cross-validation as a method for choosing a good ridge parameter
5. Regularization of feedwater flow rate evaluation for venturi meter fouling problems in nuclear power plants
6. Stochastic regularization of feedwater flow rate evaluation for venturi meter fouling problems in nuclear power plants
7. Use of kernel based techniques for sensor validation in nuclear power plants
8. Application of a model-based fault detection system to nuclear plant signals
9. Analysis of discrete ill-posed problems by means of the L-curve
10. Regularization tools: A MATLAB package for analysis and solution of discrete ill-posed problems
11. Test matrices for regularization methods
12. Rand-deficient and discrete ill-posed problems
13. Regularization methods for inferential sensing in nuclear power plants
14. Neural network regularization techniques for a sensor validation system
15. On-line sensor calibration verification: A survey
16. An evaluation of instrument calibration monitoring using artificial neural networks
17. Use of autoassociative neural networks for signal validation
18. Signal validation using an adaptive neural fuzzy inference system
19. Ridge regression: Biased estimation for nonorthogonal problems
20. Generalized information criteria in model selection
21. Nonlinear principal component analysis using autoassociative neural networks
22. Model selection
23. p
24. On the solution of functional equations by the method of regularization
25. Nonlinear PLS modeling using neural networks
26. Nonlinear partial least squares modeling for instrument surveillance and calibration verification
27. A novel approach to process modeling for instrument surveillance and calibration verification
28. Distribution of information statistics and a criterion of model fitting
29. Solution of incorrectly formulated problems and the regularization method
30. Potential application of neural networks to the operation of nuclear power plants
31. Sensor failure detection and estimation
32. Application of neural networks for sensor validation and plant monitoring
33. Development and testing of an integrated signal validation system for nuclear power plants
34. Application of information complexity in principal component regression modeling of the venturi meter drift
35. Complexity-penalized model selection for feedwater inferential measurements in nuclear power plants
36. An analysis of complexity
37. Estimation and inference by compact coding
38. Regularization methods for the multivariate state estimation technique (MSET)