Recursive System Identification Using Outlier-Robust Local Models

Jessyca Bessa1, Guilherme A. Barreto2

  • 1IFCE
  • 2Federal University of Ceará (UFC)

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Regular Session

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16:00 - 18:00 | Wed 24 Apr | Fauna | WeC2

System Identification

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Abstract

In this paper we revisit the design of neural-network based local linear models for dynamic system identification aiming at extending their use to scenarios contaminated with outliers. To this purpose, we modify well-known local linear models by replacing their original recursive rules with outlier-robust variants developed from the M -estimation framework. The performances of the proposed variants are evaluated in free simulation tasks over 3 benchmarking datasets. The obtained results corroborate the considerable improvement in the performance of the proposed models in the presence of outliers.

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