IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i9p1471-d1646174.html
   My bibliography  Save this article

Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures

Author

Listed:
  • László Kovács

    (Institute of Informatics, University of Miskolc, H-3515 Miskolc, Hungary)

Abstract

The radial basis function architecture and the multilayer perceptron architecture are very different approaches to neural networks in theory and practice. Considering their classification efficiency, both have different strengths; thus, the integration of these tools is an interesting but understudied problem domain. This paper presents a novel initialization method based on a distance-weighted homogeneity measure to construct a radial basis function network with fast convergence. The proposed radial basis function network is utilized in the development of an integrated RBF-MLP architecture. The proposed neural network model was tested in various classification tasks and the test results show superiority of the proposed architecture. The RBF-MLP model achieved nearly 40 percent better accuracy in the tests than the baseline MLP or RBF neural network architectures.

Suggested Citation

  • László Kovács, 2025. "Classification Improvement with Integration of Radial Basis Function and Multilayer Perceptron Network Architectures," Mathematics, MDPI, vol. 13(9), pages 1-25, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1471-:d:1646174
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/9/1471/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/9/1471/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cheng-Yu Ku & Chih-Yu Liu & Yu-Jia Chiu & Wei-Da Chen, 2024. "Deep Neural Networks with Spacetime RBF for Solving Forward and Inverse Problems in the Diffusion Process," Mathematics, MDPI, vol. 12(9), pages 1-19, May.
    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:13:y:2025:i:9:p:1471-:d:1646174. 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.