Time Series Recursions and Self-Tuning Control

Victor Solo
Additional contact information
Victor Solo: Harvard University

A chapter in Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, 1981, pp 178-184 from Springer

Abstract: Abstract Recursive estimates are estimates (of parameters in a time series model) that are computed in a sequential fashion (i.e. updated quickly as new observations become available). The uses of such “real” time parameter estimators include real-time forecasting and self-tuning control. Here it is shown how “real” time parameter estimators can be constructed for time series models; also an heuristic discussion of their convergence behavior is given. The analysis and synthesis of self-tuning controllers is also discussed.

Keywords: Time series; control; real time; self-tuning control; adaptive control; convergence (search for similar items in EconPapers)
Date: 1981
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-9464-8_26

Ordering information: This item can be ordered from
http://www.springer.com/9781461394648

DOI: 10.1007/978-1-4613-9464-8_26

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().