Abstract
We propose a diagnostics technique for the state space model fitting formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one. The presented subspace system identification algorithm is robust and less sensitive to outliers. We give a numerical result to show effectiveness of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | 7th IFAC Conference on Manufacturing Modelling, Management, and Control, MIM 2013 - Proceedings |
| Publisher | IFAC Secretariat |
| Pages | 1832-1837 |
| Number of pages | 6 |
| Edition | 9 |
| ISBN (Print) | 9783902823359 |
| DOIs | |
| State | Published - 2013 |
Publication series
| Name | IFAC Proceedings Volumes (IFAC-PapersOnline) |
|---|---|
| Number | 9 |
| Volume | 46 |
| ISSN (Print) | 1474-6670 |
Bibliographical note
Funding Information:⋆ This work was supported in part by King Fahd University of Petroleum and Minerals, Saudi Arabia.
ASJC Scopus subject areas
- Control and Systems Engineering