IDEAS home Printed from https://ideas.repec.org/a/abx/journl/y2024id874.html
   My bibliography  Save this article

Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks

Author

Listed:
  • D. O. Petrov

Abstract

The article describes the problems of developing neurocontrollers for controlling dynamic objects, including the complexity of forming training data sets. It is indicated that one of the known methods for training an artificial neural network controlling an object is the neuroevolutionary approach, which involves using a genetic algorithm to adjust the synaptic weighting coefficients of an artificial neural network. The idea of using a means of demonstrating the evolutionary approach to adjusting the weighting coefficients of an artificial neural network for practical training of students in the basics of the neuroevolutionary approach is proposed. Software has been developed to demonstrate the neuroevolutionary approach using the example of the evolution of an artificial neural network of a given structure intended to control a simplified computer model of an autonomous vehicle. A method for resolving the problem of stagnation when using the evolutionary approach to training an artificial neural network is described. Options for using the developed software in teaching students the basics of artificial intelligence technologies and evolutionary methods of multicriteria optimization are proposed.

Suggested Citation

  • D. O. Petrov, 2024. "Practical Approach to Studying Evolutionary Methods for Setting Weight Coefficients of Artificial Neural Networks," Digital Transformation, Educational Establishment “Belarusian State University of Informatics and Radioelectronicsâ€, vol. 30(3).
  • Handle: RePEc:abx:journl:y:2024:id:874
    DOI: 10.35596/1729-7648-2024-30-3-80-88
    as

    Download full text from publisher

    File URL: https://dt.bsuir.by/jour/article/viewFile/874/330
    Download Restriction: no

    File URL: https://libkey.io/10.35596/1729-7648-2024-30-3-80-88?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
    ---><---

    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:abx:journl:y:2024:id:874. 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: Ð ÐµÐ´Ð°ÐºÑ†Ð¸Ñ (email available below). General contact details of provider: .

    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.