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Adaptive Approximation of Uncertainty Sets for Linear Regression Models

  • A. Vicino
  • G. Zappa

Abstract

This chapter deals with the problem of uncertainty evaluation in linear regression models, representing either purely parametric models or mixed parametric/non-parametric (restricted complexity) models. The hypothesis is that disturbance information and prior knowledge on the unmodeled dynamics are available as deterministic bounds. A procedure is proposed for constructing recursively an outer bounding parallelotopic estimate of the parameter uncertainty set, which can be considered as an alternative description to commonly used ellipsoidal approximations. This new type of approximation is motivated by recent developments in the robust control field, where descriptions like hyperrectangular or polytopic domains have led to appealing stability and performance robustness properties of uncertain feedback systems.

Keywords

Linear Regression Model Supporting Hyperplane Unmodeled Dynamic Ellipsoidal Approximation Nonparametric Part 
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

  • A. Vicino
    • 1
  • G. Zappa
    • 2
  1. 1.Facoltà di IngegneriaUniversità degli Studi di SienaSienaItaly
  2. 2.Dipartimento di Sistemi e InformaticaUniversità di FirenzeFirenzeItaly

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