Convergence of continuous time stochastic ELS parameter estimation
AbstractThis paper presents continuous-time adaptive estimation schemes associated with a class of finite dimensional, time invariant, linear stochastic signal models. A global convergence theory is given for such schemes under a coloured noise/prefiller positive real condition, which may be side-stepped for moving average models. Attention is first focused on extended least squares (ELS) identification of stable signal models driven by bounded inputs. A particular feature is that weighting is introduced into the ELS scheme according to a stability measure. This weighting selection ensures that there is almost surely no finite escape time, and also there is improved transient performance in the presence of ill-conditioning. Next, some convergence results for least squares (LS) estimation of unstable signal models are extracted from the earlier theory. The ELS and LS theory suggests construction of identification schemes based on both ELS and LS. Analysis results for such are studied. The results apply within the indirect adaptive control context under reasonable controller design constraints, although details are not included in this paper.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal Stochastic Processes and their Applications.
Volume (Year): 27 (1987)
Issue (Month): ()
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.