Unifying some higher-order statistic-based methods for errors-in-variables model identification
Overview & Synopsis
In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.
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Document Information
| Document Title | Unifying some higher-order statistic-based methods for errors-in-variables model identification |
|---|---|
| Author / Organization | Stéphane Thil, Wei Xing Zheng, Marion Gilson, Hugues Garnier |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2009 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-102536 |
| Original Source Link | Visit Source Page on hal.science » |