IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i17p3206-d907096.html
   My bibliography  Save this article

A Study of Learning Issues in Feedforward Neural Networks

Author

Listed:
  • Adrian Teso-Fz-Betoño

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Ekaitz Zulueta

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Mireya Cabezas-Olivenza

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Daniel Teso-Fz-Betoño

    (Automatic Control and System Engineering Department, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

  • Unai Fernandez-Gamiz

    (Department of Nuclear and Fluid Mechanics, University of the Basque Country (UPV/EHU), 01006 Vitoria-Gasteiz, Spain)

Abstract

When training a feedforward stochastic gradient descendent trained neural network, there is a possibility of not learning a batch of patterns correctly that causes the network to fail in the predictions in the areas adjacent to those patterns. This problem has usually been resolved by directly adding more complexity to the network, normally by increasing the number of learning layers, which means it will be heavier to run on the workstation. In this paper, the properties and the effect of the patterns on the network are analysed and two main reasons why the patterns are not learned correctly are distinguished: the disappearance of the Jacobian gradient on the processing layers of the network and the opposite direction of the gradient of those patterns. A simplified experiment has been carried out on a simple neural network and the errors appearing during and after training have been monitored. Taking into account the data obtained, the initial hypothesis of causes seems to be correct. Finally, some corrections to the network are proposed with the aim of solving those training issues and to be able to offer a sufficiently correct prediction, in order to increase the complexity of the network as little as possible.

Suggested Citation

  • Adrian Teso-Fz-Betoño & Ekaitz Zulueta & Mireya Cabezas-Olivenza & Daniel Teso-Fz-Betoño & Unai Fernandez-Gamiz, 2022. "A Study of Learning Issues in Feedforward Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3206-:d:907096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/17/3206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/17/3206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Manjula Devi & S. Kuppuswami & R. C. Suganthe, 2013. "Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      ;
      ;
      ;

      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:gam:jmathe:v:10:y:2022:i:17:p:3206-:d:907096. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      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.