Recursive Estimation Algorithms for Linear Models with Set Membership Error

G. Belforte and T. T. Tay
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G. Belforte: Politecnico di Torino, Dipartimento di Automatica e Informatica
T. T. Tay: National University of Singapore, Department of Electrical Engineering

Chapter 6 in Bounding Approaches to System Identification, 1996, pp 83-99 from Springer

Abstract: Abstract This chapter reviews some of the more recent algorithms for sequential parameter identification in the context of unknown but bounded measurement errors when the model output is linear in the parameters. The properties of the different algorithms are analyzed and compared. The possibility of evaluating the confidence of the obtained estimates is discussed, particularly information required on the noise structure in order to assess the confidence of the estimates is shown. Finally, the possibility of using the algorithms for time-varying system identification is considered and the case of uncertain regressors is addressed.

Keywords: Central Estimate; Average Absolute Error; Exact Description; Projection Estimate; Past Measurement (search for similar items in EconPapers)
Date: 1996
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4757-9545-5_6

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DOI: 10.1007/978-1-4757-9545-5_6

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