Deep Learning in a System Identification Perspective

  • Reference work entry
  • First Online:
Encyclopedia of Systems and Control

Abstract

The use of deep learning for sequence learning problems and system identification are intimately linked, and interesting opportunities exist on this cross section. The aim of this chapter is to briefly introduce deep learning from a system identification perspective and to explain some of the most apparent links between these two topics.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 1,604.99
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 1,793.49
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Bibliography

  • Andersson C, Ribeiro AH, Tiels K, Wahlström N, Schön TB (2019) Deep convolutional networks are useful in system identification. In: Proceedings of the IEEE 58th IEEE Conference on Decision and Control (CDC), Nice

    Google Scholar 

  • Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Technical report. ar**v:1803.01271

    Google Scholar 

  • Bottou L, Curtis FE, Nocedal J (2018) Optimization methods for large-scale machine learning. SIAM Rev 60(2):223–311

    Article  MathSciNet  MATH  Google Scholar 

  • Boulanger-Lewandowski N, Bengio Y, Vincent P (2012) Modeling temporal dependencies in high-dimensional sequences: application to polyphonic music generation and transcription. In: Proceedings of the 29 th International Conference on Machine Learning (ICML), Edinburgh

    Google Scholar 

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Belmont, Wadsworth

    MATH  Google Scholar 

  • Cho K, Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha

    Google Scholar 

  • Chung J, Kastner K, Dinh L, Goel K, Courville AC, Bengio Y (2015) A recurrent latent variable model for sequential data. In: Advances in Neural Information Processing Systems (NIPS)

    Google Scholar 

  • Fabius O, van Amersfoort JR, Kingma DP (2014) Variational recurrent auto-encoders. Technical report. ar**v:1412.6581

    Google Scholar 

  • Geman S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4(1):1–58

    Article  Google Scholar 

  • Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge/London

    MATH  Google Scholar 

  • Hinton G, Deng L, Yu D, Dahl GE, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN, Kingsbury B (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97

    Article  Google Scholar 

  • Hochreiter S, Schmidthuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems NIPS, pp 1097–1105

    Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  • Ljung L (1999) System identification – theory for the user, 2nd edn. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115–133

    Article  MathSciNet  MATH  Google Scholar 

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. Technical report. ar**v:1301.3781

    Google Scholar 

  • Osendorfer C, Bayer J (2014) Learning stochastic recurrent networks. Technical report. ar**v:1411.7610

    Google Scholar 

  • Pascanu R, Gulcehre C, Cho K, Bengio Y (2014) How to construct deep recurrent neural networks. In: Proceedings of the 2nd International Conference on Learning Representations (ICLR)

    Google Scholar 

  • Rangapuram SS, Seeger MW, Gasthaus J, Stella L, Wang Y, Januschowski T (2018) Deep state space models for time series forecasting. In: Advances in Neural Information Processing Systems (NIPS), pp 7785–7794

    Google Scholar 

  • Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22(3):400–407

    Article  MathSciNet  MATH  Google Scholar 

  • Schoukens J, Ljung L (2019) Nonlinear system identification – a user-oriented roadmap. IEEE Control Syst Mag 39(6):28–99. https://doi.org/10.1109/MCS.2019.2938121, IEEE https://ieeexplore.ieee.org/document/8897147

  • Schoukens J, Vaes M, Pintelon R (2016) Linear system identification in a nonlinear setting: nonparametric analysis of the nonlinear distortion and their impact on the best linear approximation. IEEE Control Syst Mag 36:38–69

    Article  MathSciNet  Google Scholar 

  • Williams RJ, Hinton GE, Rumelhart DE (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Thomas B. Schön or Lennart Ljung .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Schön, T., Ljung, L. (2021). Deep Learning in a System Identification Perspective. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, Cham. https://doi.org/10.1007/978-3-030-44184-5_100089

Download citation

Publish with us

Policies and ethics

Navigation