Fourier neural networks: A comparative study

Malika Uteuliyeva, Abylay Zhumekenov, Rustem Takhanov, Zhenisbek Assylbekov*, Alejandro J. Castro, Olzhas Kabdolov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

We review neural network architectures which were motivated by Fourier series and integrals and which are referred to as Fourier neural networks. These networks are empirically evaluated in synthetic and real-world tasks. Neither of them outperforms the standard neural network with sigmoid activation function in the real-world tasks. All neural networks, both Fourier and the standard one, empirically demonstrate lower approximation error than the truncated Fourier series when it comes to approximation of a known function of multiple variables.

Original languageEnglish (US)
Pages (from-to)1107-1120
Number of pages14
JournalIntelligent Data Analysis
Volume24
Issue number5
DOIs
StatePublished - 2020

Keywords

  • convergence
  • Fourier series
  • function approximation
  • Neural networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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