Nesrine Wagaa, Hichem Kallel, 2020 International Conference on Control, Automation and Diagnosis (ICCAD), 2020, 1
Saeed Panahian Fard, Zarita Zainuddin, Nature-Inspired Computing, 2017, 1456
J.M. Almira, P.E. Lopez-de-Teruel, D.J. Romero-López, F. Voigtlaender, “Negative results for approximation using single layer and multilayer feedforward neural networks”, Journal of Mathematical Analysis and Applications, 494, № 1, 2021, 124584
Maxim Secor, Alexander V. Soudackov, Sharon Hammes-Schiffer, “Artificial Neural Networks as Mappings between Proton Potentials, Wave Functions, Densities, and Energy Levels”, J. Phys. Chem. Lett., 12, № 9, 2021, 2206
Rafael Rodolfo de Melo, Eder Pereira Miguel, “USE OF ARTIFICIAL NEURAL NETWORKS IN PREDICTING PARTICLEBOARD QUALITY PARAMETERS”, Rev. Árvore, 40, № 5, 2016, 949
Ziwei Xiao, Wenjie Gang, Jiaqi Yuan, Zhuolun Chen, Ji Li, Xuan Wang, Xiaomei Feng, “Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning”, Energy and Buildings, 258, 2022, 111832
Xia Liu, “Approximating smooth and sparse functions by deep neural networks: Optimal approximation rates and saturation”, Journal of Complexity, 79, 2023, 101783
Saeed Panahian Fard, Zarita Zainuddin, 355, Proceedings of the 4th International Conference on Computer Engineering and Networks, 2015, 187
Danilo Costarelli, Gianluca Vinti, “Pointwise and uniform approximation by multivariate neural network operators of the max-product type”, Neural Networks, 81, 2016, 81
Xia Liu, Di Wang, Shao-Bo Lin, “Construction of Deep ReLU Nets for Spatially Sparse Learning”, IEEE Trans. Neural Netw. Learning Syst., 34, № 10, 2023, 7746