@inproceedings{a43044fe889540a288a2d9b84f3f9ceb,
title = "Partial Least Squares Regression for Recursive System Identification",
abstract = "Industrial processes usually involve a large number of variables, many of which vary in a correlated manner. To identify a process model which has correlated variables, an ordinary least squares approach demonstrates ill-conditioned problem and the resulting model is sensitive to changes in sampled data. In this paper, a recursive partial least squares (PLS) regression is used for on-line system identification and circumventing the ill-conditioned problem. The partial least squares method is used to remove the correlation by projecting the original variable space to an orthogonal latent space. Applications of the proposed algorithm to a chemical process modeling problem is discussed.",
author = "QIN, \{S. Joe\}",
year = "1993",
month = dec,
doi = "10.1109/cdc.1993.325671",
language = "English",
isbn = "0780312988",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "2617--2621",
booktitle = "Proceedings of 32nd IEEE Conference on Decision and Control",
address = "United States",
note = "32nd IEEE Conference on Decision and Control ; Conference date: 15-12-1993 Through 17-12-1993",
}