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Worst-case analysis of the least-squares method and related identification methods
School of Mathematics, University of Leeds.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
1995 (English)In: Systems & control letters (Print), ISSN 0167-6911, E-ISSN 1872-7956, Vol. 24, no 3, p. 193-200Article in journal (Refereed) Published
Abstract [en]

We consider worst-case analysis of system identification under less restrictive assumptions on the noise than the l∞ bounded error condition. It is shown that the least-squares method has a robust convergence property in l2 identification, but lacks a corresponding property in l1 identification (as well as in all other non-Hilbert space settings). The latter result is in stark contrast with typical results in asymptotic stochastic analysis of the least-squares method. Furthermore, it is shown that the Khintchine inequality is useful in the analysis of least lp identification methods

Place, publisher, year, edition, pages
1995. Vol. 24, no 3, p. 193-200
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-12847Local ID: bfe8de60-bf2c-11df-a707-000ea68e967bOAI: oai:DiVA.org:ltu-12847DiVA, id: diva2:985798
Note

Godkänd; 1995; 20100913 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-02-06Bibliographically approved

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