IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v405y2021ics0096300321003325.html
   My bibliography  Save this article

Properties and computation of continuous-time solutions to linear systems

Author

Listed:
  • Stanimirović, Predrag S.
  • Katsikis, Vasilios N.
  • Jin, Long
  • Mosić, Dijana

Abstract

We investigate solutions to the system of linear equations (SoLE) in both the time-varying and time-invariant cases, using both gradient neural network (GNN) and Zhang neural network (ZNN) designs. Two major limitations should be overcome. The first limitation is the inapplicability of GNN models in time-varying environment, while the second constraint is the possibility of using the ZNN design only under the presence of invertible coefficient matrix. In this paper, by overcoming the possible limitations, we suggest, in all possible cases, a suitable solution for a consistent or inconsistent linear system. Convergence properties are investigated as well as exact solutions.

Suggested Citation

  • Stanimirović, Predrag S. & Katsikis, Vasilios N. & Jin, Long & Mosić, Dijana, 2021. "Properties and computation of continuous-time solutions to linear systems," Applied Mathematics and Computation, Elsevier, vol. 405(C).
    • Handle: RePEc:eee:apmaco:v:405:y:2021:i:c:s0096300321003325
    DOI: 10.1016/j.amc.2021.126242
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321003325
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2021.126242?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. Predrag S. Stanimirović & Spyridon D. Mourtas & Vasilios N. Katsikis & Lev A. Kazakovtsev & Vladimir N. Krutikov, 2022. "Recurrent Neural Network Models Based on Optimization Methods," Mathematics, MDPI, vol. 10(22), pages 1-26, November.

    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:eee:apmaco:v:405:y:2021:i:c:s0096300321003325. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.