Journal article
Convergence Properties of the Membership Set
Automatica (Oxford), Vol.34(10), pp.1245-1249
1998
DOI: 10.1016/S0005-1098(98)00065-X
Abstract
In this paper, we study the convergence properties of the membership set for system identification in the presence of unknown but bounded noise. Besides the classical
pointwise noise model, we also analyze the so-called
window and
average models. Two different scenarios are considered: (1) the bound (tight or overestimated) is known; (2) the bound is unknown and has to be estimated from measurement data. Under some probabilistic assumptions on the noise sequence and with the persistent excitation regressor, in the first case we prove that the diameter of the associated membership sets converges to zero with probability one if the noise bound is tight. If the bound is overestimated, we compute upper bounds for the diameter which are stated in terms of the difference between the true and the over-estimated bound. In the second case, we show that the estimates of the bound converge to the true but unknown noise bound.
Details
- Title: Subtitle
- Convergence Properties of the Membership Set
- Creators
- ER-WEI BAI - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242, USAHYONYONG CHO - Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa 52242, USAROBERTO TEMPO - CENS-CNR, Politecnico di Torino, Torino, Italy
- Resource Type
- Journal article
- Publication Details
- Automatica (Oxford), Vol.34(10), pp.1245-1249
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/S0005-1098(98)00065-X
- ISSN
- 0005-1098
- eISSN
- 1873-2836
- Language
- English
- Date published
- 1998
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984083230602771
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