Partial Least Squares algorithm (PLS) is widely used in chemometric studies and increasingly in process engineering applications. For process problems, PLS is typically used as a dynamic modeling technique. Unfortunately, traditional PLS identification techniques will typically produce a model that is biased in its regression parameters. In this article, an effective unbiased recursive PLS algorithm is proposed to address this problem. This paper provides a comparative study, using simulated data, to illustrate the potential benefits of the proposed approach.
|Title of host publication||Proceedings of the IASTED International Conference on Modelling, Identification and Control|Proc. IASTED Int. Conf. Model. Ident. Control|
|Number of pages||5|
|Publication status||Published - 2010|
|Event||29th IASTED International Conference on Modelling, Identification and Control, MIC 2010 - Innsbruck|
Duration: 1 Jul 2010 → …
|Conference||29th IASTED International Conference on Modelling, Identification and Control, MIC 2010|
|Period||1/07/10 → …|
- System identification, ordinary least squares, partial least squares, recursive identification.