IDEAS home Printed from https://ideas.repec.org/a/taf/nmcmxx/v27y2021i1p246-262.html
   My bibliography  Save this article

Designing optimal models of nonlinear MIMO systems based on orthogonal polynomial neural networks

Author

Listed:
  • Marko Milojković
  • Miroslav Milovanović
  • Saša S. Nikolić
  • Miodrag Spasić
  • Andjela Antić

Abstract

This paper presents a new method for modelling of dynamic systems by using specially designed orthogonal polynomial neural networks. These networks utilize the feature that the basis made of orthogonal functions can be used for approximation of arbitrary function, while their property of orthogonality enables optimal performances in the sense of both convergence time and approximation error. In this regard, generalized quasi-orthogonal polynomials, specifically tailored for the application in the modelling of complex dynamic systems with time-varying behaviour, are considered. Adaptivity of the designed model is achieved by using variable factors inside the orthogonal basis. The designed orthogonal neural network is applied in modelling of laboratory twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems. Detailed comparative analysis is performed for a different number of polynomials in expansion with the purpose of finding the optimal model in the sense of trade-off between model accuracy and complexity.

Suggested Citation

  • Marko Milojković & Miroslav Milovanović & Saša S. Nikolić & Miodrag Spasić & Andjela Antić, 2021. "Designing optimal models of nonlinear MIMO systems based on orthogonal polynomial neural networks," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 27(1), pages 246-262, January.
    • Handle: RePEc:taf:nmcmxx:v:27:y:2021:i:1:p:246-262
    DOI: 10.1080/13873954.2021.1909069
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13873954.2021.1909069
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13873954.2021.1909069?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.

    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:27:y:2021:i:1:p:246-262. 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.