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An Empirical Evaluation of Robust Gaussian Process Models for System Identification

  • César Lincoln C. Mattos
  • José Daniel A. Santos
  • Guilherme A. Barreto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

System identification comprises a number of linear and nonlinear tools for black-box modeling of dynamical systems, with applications in several areas of engineering, control, biology and economy. However, the usual Gaussian noise assumption is not always satisfied, specially if data is corrupted by impulsive noise or outliers. Bearing this in mind, the present paper aims at evaluating how Gaussian Process (GP) models perform in system identification tasks in the presence of outliers. More specifically, we compare the performances of two existing robust GP-based regression models in experiments involving five benchmarking datasets with controlled outlier inclusion. The results indicate that, although still sensitive in some degree to the presence of outliers, the robust models are indeed able to achieve lower prediction errors in corrupted scenarios when compared to conventional GP-based approach.

Keywords

Robust system identification Gaussian process Approximate Bayesian inference 

Notes

Acknowledgments

The authors thank the financial support of FUNCAP, IFCE, NUTEC and CNPq (grant no. 309841/2012-7).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • César Lincoln C. Mattos
    • 1
  • José Daniel A. Santos
    • 2
  • Guilherme A. Barreto
    • 1
  1. 1.Department of Teleinformatics Engineering, Center of TechnologyFederal University of CearáFortalezaBrazil
  2. 2.Department of IndustryFederal Institute of Education, Science and Technology of CearáMaracanaúBrazil

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