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Articulated Estimator Random Field and Geometrical Approach Applied in System Identification

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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.

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Correspondence to Christophe Corbier.

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This paper was recommended for publication by Editor LIU Yungang.

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Corbier, C. Articulated Estimator Random Field and Geometrical Approach Applied in System Identification. J Syst Sci Complex 31, 1164–1185 (2018). https://doi.org/10.1007/s11424-018-6223-z

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  • DOI: https://doi.org/10.1007/s11424-018-6223-z

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