Correction: Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network

Correction on: To overcome these challenges, this paper utilizes the Exponential Linear Units (ELU) activation function, introduced by Clevert et al., in 2016. ELU maintains nonlinearity while providing a better handling of negative inputs, avoiding the "dying ReLU" problem (Staer-Jensen et al., 2018). This characteristic has made ELU popular in deep neural networks, particularly in natural language processing and image processing, where it has achieved notable results. The reference for [Staer-Jensen et al., 2018] was erroneously written as [Staer-Jensen, H., Sunde, K., Nakstad, E. R., Eritsland, J., and Andersen, G. Ø . (2018). Comparison of Three Haemodynamic Monitoring Methods in Comatose Post Cardiac Arrest Patients. Scand. Cardiovasc. J. 52 (3), 141–148. doi:10.1080/14017431.2018.1450992]. It should be [Clevert, D.-A., Untertiner, T., and Hochreiter, S. (2016). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv preprint. arXiv: 1511.07289, 4.5, 11].

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Publication Details

Journal
Frontiers in Energy Research
Published
2025-10-24
DOI
https://doi.org/10.3389/fenrg.2025.1707498
Primary Topic
Advanced Algorithms and Applications
Type
article
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0.00
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article

Correction: Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network

Zhimin Na, Baolin Liu, Lei Zhang, Zhijian Liu et al.
Frontiers in Energy Research
Advanced Algorithms and Applications
article

Correction: Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network

Zhimin Na, Baolin Liu, Lei Zhang, Zhijian Liu, Guanchao Zhao, Shuang Zhao, Zhongming Yu, Lingyi Wang, Wei He
article en

Abstract

Correction on: To overcome these challenges, this paper utilizes the Exponential Linear Units (ELU) activation function, introduced by Clevert et al., in 2016. ELU maintains nonlinearity while providing a better handling of negative inputs, avoiding the "dying ReLU" problem (Staer-Jensen et al., 2018). This characteristic has made ELU popular in deep neural networks, particularly in natural language processing and image processing, where it has achieved notable results. The reference for [Staer-Jensen et al., 2018] was erroneously written as [Staer-Jensen, H., Sunde, K., Nakstad, E. R., Eritsland, J., and Andersen, G. Ø . (2018). Comparison of Three Haemodynamic Monitoring Methods in Comatose Post Cardiac Arrest Patients. Scand. Cardiovasc. J. 52 (3), 141–148. doi:10.1080/14017431.2018.1450992]. It should be [Clevert, D.-A., Untertiner, T., and Hochreiter, S. (2016). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). arXiv preprint. arXiv: 1511.07289, 4.5, 11].

Frontiers in Energy ResearchVol. 13
Kunming University of Science and Technology (CN), Qujing Normal University (CN)
Openalex Percentile: Top 61%
Advanced Algorithms and Applications
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Correction: Short-time photovoltaic output prediction method based on depthwise separable convolution Visual Geometry group- deep gate recurrent neural network — Zhimin Na, Baolin Liu, et al. · Frontiers in Energy Research (2025) | TGRS Research Map | TGRS