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.
|Name||Proceedings of the IEEE Conference on Decision and Control|
|Publisher||Institute of Electrical and Electronics Engineers|
|Conference||32nd IEEE Conference on Decision and Control|
|Period||15/12/93 → 17/12/93|