Zhumekenov, AbylayUteuliyeva, MalikaTakhanov, RustemAssylbekov, ZhenisbekCastro, Alejandro J.2022-07-142022-07-142019Uteuliyeva, 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-195050http://nur.nu.edu.kz/handle/123456789/6432We 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.enAttribution-NonCommercial-ShareAlike 3.0 United StatesType of access: Open Accessneural network architecturesFourier neural networksArticle