IDEAS home Printed from https://ideas.repec.org/a/taf/nmcmxx/v11y2005i4p411-424.html
   My bibliography  Save this article

Dynamic systems identification with Gaussian processes

Author

Listed:
  • Juš Kocijan
  • Agathe Girard
  • Blaž Banko
  • Roderick Murray-Smith

Abstract

This paper describes the identification of nonlinear dynamic systems with a Gaussian process (GP) prior model. This model is an example of the use of a probabilistic non-parametric modelling approach. GPs are flexible models capable of modelling complex nonlinear systems. Also, an attractive feature of this model is that the variance associated with the model response is readily obtained, and it can be used to highlight areas of the input space where prediction quality is poor, owing to the lack of data or complexity (high variance). We illustrate the GP modelling technique on a simulated example of a nonlinear system.

Suggested Citation

  • Juš Kocijan & Agathe Girard & Blaž Banko & Roderick Murray-Smith, 2005. "Dynamic systems identification with Gaussian processes," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 11(4), pages 411-424, December.
    • Handle: RePEc:taf:nmcmxx:v:11:y:2005:i:4:p:411-424
    DOI: 10.1080/13873950500068567
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13873950500068567
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13873950500068567?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christos Merkatas & Simo Särkkä, 2023. "System identification using autoregressive Bayesian neural networks with nonparametric noise models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 319-330, May.
    2. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:nmcmxx:v:11:y:2005:i:4:p:411-424. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/NMCM20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.