Regularization of Ill-Posed Surveillance and Diagnostic Measurements

  • Andrei V. Gribok
  • J. Wesley Hines
  • Aleksey Urmanov
  • Robert E. Uhrig
Part of the Power Systems book series (POWSYS)


Most data-based predictive modeling techniques have an inherent weakness in that they may give unstable or inconsistent results when the predictor data is highly correlated. Predictive modeling problems of this design are usually under constrained and are termed ill-posed. This paper presents several examples of ill-posed diagnostic problems and regularization methods necessary for getting accurate and consistent prediction results. The examples include plant-wide sensor calibration monitoring and the inferential sensing of nuclear power plant feedwater flow using neural networks, and non-linear partial least squares techniques, and linear regularization techniques implementing ridge regression and informational complexity measures.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Andrei V. Gribok
    • 1
  • J. Wesley Hines
    • 1
  • Aleksey Urmanov
    • 1
  • Robert E. Uhrig
    • 1
  1. 1.The University of TennesseeNuclear Engineering DepartmentKnoxvilleUSA

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