# Regularization Methods for Inferential Sensing in Nuclear Power Plants

Chapter

## Abstract

Inferential sensing is the use of information related to a plant parameter to infer its actual value. The most common method of inferential sensing uses a mathematical model to infer a parameter value from correlated sensor values. Collinearity in the predictor variables leads to an ill posed problem that causes inconsistent results when data based models such as linear regression and neural networks are used. This chapter presents several linear and non-linear inferential sensing methods including linear regression and neural networks. Both of these methods can be modified from their original form to solve ill posed problems and produce more consistent results.

## Keywords

Partial Little Square Nuclear Power Plant Singular Value Decomposition Hide Neuron Tikhonov Regularization
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