A novel algorithm is presented for the design of inferential estimators forprocess monitoring and control. The algorithm aims at integrating Partial Least Squares (PLS) techniques and Subspace Identification Methods (SIM) to exploit the main advantages of both methodologies. In particular, the algorithm will retain the PLS computational robustness in dealing with large sets of correlated inputs and outputs, whilst profiting by the SIM dynamic description of the system being investigated.
A framework for PLS-SIM integration
MURADORE, Riccardo;
2006-01-01
Abstract
A novel algorithm is presented for the design of inferential estimators forprocess monitoring and control. The algorithm aims at integrating Partial Least Squares (PLS) techniques and Subspace Identification Methods (SIM) to exploit the main advantages of both methodologies. In particular, the algorithm will retain the PLS computational robustness in dealing with large sets of correlated inputs and outputs, whilst profiting by the SIM dynamic description of the system being investigated.
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