Please use this identifier to cite or link to this item:
http://hdl.handle.net/2282/396
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| Title: | Dynamic system multivariate calibration based on multirate sampling data |
| Authors: | Ergon, Rolf
Halstensen, Maths |
| Issue Date: | 2001 |
| Abstract: | The statistical principal component regression (PCR) and chemometric partial
least squares regression (PLSR) algorithms based on latent variables (LV)
modeling are effective tools for handling ill-conditioned regression data. In many
process related cases the data form time series, and it may then be possible to
improve the prediction/estimation results by utilizing the autocorrelation in the
observations. This can be done by use of estimators found from experimental data
by use of a combination of statistical/chemometric and system identification
methods. In important industrial cases, the response variables are product qualities
which also in the experimental data are sampled at a low and possibly irregular
rate, while the regressor variables are sampled at a higher rate. After a discussion
of the options available, the paper shows how the autocorrelation of the regressor
variables in such multirate sampling cases may be utilized by identification of
latent variables based output error (LV + OE) estimators. An example using
acoustic power spectrum regressor data is finally presented. |
| Keywords: | Multivariate calibration |
| Publisher: | Norsk forening for automatisering |
| Document type: | Journal article |
| URI: | http://hdl.handle.net/2282/396 |
| Appears in Collections: | Institutt for elektro, IT og kybernetikk
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