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Partial Least Squares

  • Evan L. Russell
  • Leo H. Chiang
  • Richard D. Braatz
Part of the Advances in Industrial Control book series (AIC)

Abstract

Partial Least Squares (PLS), also known as Projection to Latent Structures, is a dimensionality reduction technique for maximizing the covariance between the predictor (independent) matrix X and the predicted (dependent) matrix Y for each component of the reduced space [61, 235]. A popular application of PLS is to select the matrix Y to contain only product quality data which can even include off-line measurement data, and the matrix X to contain all other process variables [144]. Such inferential models (also known as soft sensors) can be used for the on-line prediction of the product quality data [149, 155, 156], for incorporation into process control algorithms [106, 181, 182], as well as for process monitoring [144, 181, 182]. Discriminant PLS selects the matrix X to contain all process variables and selects the Y matrix to focus PLS on the task of fault diagnosis [26].

Keywords

Partial Little Square Fault Diagnosis Partial Little Square Model Score Vector Soft Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2000

Authors and Affiliations

  • Evan L. Russell
    • 1
  • Leo H. Chiang
    • 2
  • Richard D. Braatz
    • 2
  1. 1.Exxon Production Research CompanyHoustonUSA
  2. 2.Department of Chemical EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

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