FOURIER NEURAL NETWORKS: A COMPARATIVE STUDY
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Date
2019
Authors
Zhumekenov, Abylay
Uteuliyeva, Malika
Takhanov, Rustem
Assylbekov, Zhenisbek
Castro, Alejandro J.
Journal Title
Journal ISSN
Volume Title
Publisher
arxiv
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.
Description
Keywords
Type of access: Open Access, neural network architectures, Fourier neural networks
Citation
Uteuliyeva, M., Zhumekenov, A., Takhanov, R., Assylbekov, Z., Castro, A. J., & Kabdolov, O. (2020). Fourier neural networks: A comparative study. Intelligent Data Analysis, 24(5), 1107–1120. https://doi.org/10.3233/ida-195050