Skip to main content

Non-dissipative Reservoir Computing Approaches for Time-Series Classification

  • Conference paper
  • First Online:
Artificial Neural Networks and Machine Learning – ICANN 2024 (ICANN 2024)

Abstract

Reservoir Computing (RC) is a consolidated framework for designing fastly trainable recurrent neural systems, where the dynamical component is fixed and initialized to implement a fading memory over the input signal. In this paper, we study the behavior of a recently introduced class of alternative RC approaches in which the fixed dynamical component implements a stable but non-dissipative system, so that the driving temporal signal can be propagated through multiple time steps effectively. We analyze the behavior of two classes of non-dissipative RC in terms of dynamical stability and show the resulting advantages in time-series classification tasks in comparison to conventional RC.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    A matrix \(\textbf{M}\) is antisymmetric if \(\textbf{M}^T = -\textbf{M}\).

  2. 2.

    www.timeseriesclassification.com.

References

  1. Bauer, F.L., Fike, C.T.: Norms and exclusion theorems. Numer. Math. 2(1), 137–141 (1960)

    MathSciNet  Google Scholar 

  2. Ceni, A., Gallicchio, C.: Residual reservoir computing neural networks for time-series classification. In: ESANN (2023)

    Google Scholar 

  3. Ceni, A., Gallicchio, C.: Residual echo state networks: residual recurrent neural networks with stable dynamics and fast learning. Neurocomputing 127966 (2024)

    Google Scholar 

  4. Gallicchio, C.: Euler state networks: Non-dissipative reservoir computing. Neurocomputing 127411 (2024)

    Google Scholar 

  5. Gallicchio, C., Micheli, A.: Architectural and Markovian factors of echo state networks. Neural Netw. 24(5), 440–456 (2011)

    Google Scholar 

  6. Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. Inverse Prob. 34(1), 014004 (2017)

    MathSciNet  Google Scholar 

  7. Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science (2004)

    Google Scholar 

  8. Jaeger, H., Lukoševičius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335–352 (2007)

    Google Scholar 

  9. Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. German National Research Center for Information Technology GMD Technical Report, Bonn, Germany, vol. 148, no. 34, p. 13 (2001)

    Google Scholar 

  10. Lukoševičius, M.: A practical guide to applying echo state networks. In: Montavon, G., Orr, G.B., Müller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 659–686. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-35289-8_36

    Google Scholar 

  11. Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127–149 (2009)

    Google Scholar 

  12. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)

    Google Scholar 

  13. Nakajima, K., Fischer, I.: Reservoir Computing. Springer, Singapore (2021). https://doi.org/10.1007/978-981-13-1687-6

    Google Scholar 

  14. Tanaka, G., et al.: Recent advances in physical reservoir computing: a review. Neural Netw. 115, 100–123 (2019)

    Google Scholar 

  15. Yildiz, I.B., Jaeger, H., Kiebel, S.J.: Re-visiting the echo state property. Neural Netw. 35, 1–9 (2012)

    Google Scholar 

Download references

Acknowledgments

This work has been supported by EU-EIC EMERGE (Grant No. 101070918), and by NEURONE, a project funded by the Italian Ministry of University and Research (PRIN 20229JRTZA).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Ceni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gallicchio, C., Ceni, A. (2024). Non-dissipative Reservoir Computing Approaches for Time-Series Classification. In: Wand, M., Malinovská, K., Schmidhuber, J., Tetko, I.V. (eds) Artificial Neural Networks and Machine Learning – ICANN 2024. ICANN 2024. Lecture Notes in Computer Science, vol 15025. Springer, Cham. https://doi.org/10.1007/978-3-031-72359-9_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72359-9_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72358-2

  • Online ISBN: 978-3-031-72359-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics