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Journal of Systems Science and Complexity

, Volume 31, Issue 5, pp 1164–1185 | Cite as

Articulated Estimator Random Field and Geometrical Approach Applied in System Identification

  • Christophe Corbier
Article
  • 12 Downloads

Abstract

A new point of view of robust statistics based on a geometrical approach is tackled in this paper. Estimation procedures are carried out from a new robust cost function based on a chaining of elementary convex norms. This chain is randomly articulated in order to treat more efficiently natural outliers in data-set. Estimated parameters are considered as random fields and each of them, named articulated estimator random field (AERF) is a manifold or stratum of a stratified space with Riemannian geometry properties. From a high level excursion set, a probability distribution model Msta is presented and a system model validation geometric criterion (SYMOVAGEC) for system model structures Msys based on Riccian scalar curvatures is proposed. Numerical results are drawn in a context of system identification.

Keywords

Articulated robust estimation estimators random field information geometry stratified space system identification 

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Copyright information

© Institute of Systems Science, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Lyon University, St-Etienne Jean Monnet University, Roanne Technology University InstituteRoanne cedexFrance

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