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Robust Echo State Network for Recursive System Identification

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Advances in Computational Intelligence (IWANN 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11506))

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Abstract

The use of recurrent neural networks in online system identification is very limited in real-world applications, mainly due to the propagation of errors caused by the iterative nature of the prediction task over multiple steps ahead. Bearing this in mind, in this paper, we revisit design issues regarding the robustness of the echo state network (ESN) model in such online learning scenarios using a recursive estimation algorithm and an outlier robust-variant of it. By means of a comprehensive set of experiments, we show that the performance of the ESN is dependent on the adequate choice of the feedback pathways and that the prediction instability is amplified by the norm of the output weight vector, an often neglected issue in related studies.

This study was financed by the following Brazilian research funding agencies: CAPES (finance code 001), FUNCAP (BMD-008-01413.01.02-17) and CNPq (grant 309451/2015-9).

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Notes

  1. 1.

    That of relating asymptotic properties of the excited reservoir dynamics to the driving signal.

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Author information

Authors and Affiliations

  1. Department of Teleinformatics Engineering, Federal University of Ceará, Center of Technology, Campus of Pici, Fortaleza, Ceará, Brazil

    Renan Bessa & Guilherme A. Barreto

Authors
  1. Renan Bessa
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  2. Guilherme A. Barreto
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Corresponding author

Correspondence to Guilherme A. Barreto .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Bessa, R., Barreto, G.A. (2019). Robust Echo State Network for Recursive System Identification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_21

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20520-1

  • Online ISBN: 978-3-030-20521-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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