Recursive Estimation Algorithms for Linear Models with Set Membership Error

  • G. Belforte
  • T. T. Tay

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • G. Belforte
    • 1
  • T. T. Tay
    • 2
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTorinoItaly
  2. 2.Department of Electrical EngineeringNational University of SingaporeSingapore

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