The size of the membership-set in a probabilistic framework

Authors:
HüSeyin AkçAy
Affiliations:
Department of Electrical and Electronics Engineering, Anadolu University, Eskişehir 26470, Turkey
Venue:
Automatica (Journal of IFAC)
Year:
2004

Citing 2
Cited 1

Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor for systems with bounded noise

IEEE Transactions on Information Theory
Brief Paper: Convergence Properties of the Membership Set

Automatica (Journal of IFAC)

Membership set identification with periodic inputs and orthonormal regressors

Signal Processing - Special section: Multimodal human-computer interfaces

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Abstract

In this paper, we study the size of the membership-set for system identification in a probabilistic framework. Assuming that the regressors are persistently exciting and the measurement noise is a sequence of independent, identically distributed bounded random variables, lower and upper non-asymptotic probability bounds on the membership-set diameter are obtained. These bounds are used in the computation of the confidence intervals for interpolatory estimators.