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Modifications of the Givens Training Algorithm for Artificial Neural Networks

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11508))

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Abstract

The Givens algorithm is a supervised training method for neural networks. This paper presents several optimization techniques that could be applied on the top of the Givens algorithm. First, the classic variant of the Givens method is briefly described. The main section of the article contains a detailed description of the proposed retry worst samples, skip best samples, and the Givens epoch update optimization techniques. The paper concludes with the simulation results and an overall summary.

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Author information

Authors and Affiliations

  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Jarosław Bilski & Bartosz Kowalczyk

  2. Information Technology Institute, University of Social Sciences, Łódź, Poland

    Andrzej Cader

  3. Clark University, Worcester, MA, 01610, USA

    Andrzej Cader

Authors
  1. Jarosław Bilski
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  2. Bartosz Kowalczyk
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  3. Andrzej Cader
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Corresponding author

Correspondence to Jarosław Bilski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Bilski, J., Kowalczyk, B., Cader, A. (2019). Modifications of the Givens Training Algorithm for Artificial Neural Networks. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11508. Springer, Cham. https://doi.org/10.1007/978-3-030-20912-4_2

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  • DOI: https://doi.org/10.1007/978-3-030-20912-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20911-7

  • Online ISBN: 978-3-030-20912-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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