Correction: Grey wolf optimization–based deep echo state network for time series prediction
[Be that as it may, the execution of the GWODESN is superior than that of the conventional ESN.] A correction has been made to the section [4 Experimental setup, results, and discussion,4.3 Mackey-Glass System]: "[Be that as it may, the performance of the GWODESN is superior to that of the conventional ESN]"[Figure 10 appears that the GWODESN show has superior forecast precision and soundness than other models.] A correction has been made to the section [4 Experimental setup, results, and discussion,4.3 Mackey-Glass System]: "[Figure 10 shows that the GWODESN has superior forecast precision and soundness to other models.]" [Comparative test that comes about on four time-series forecast assignments clearly illustrates that the proposed GWODESN outflank the ELM, LSTM, and ESN benchmarks] [Within the future, we will center on progressing the network topology and apply the model in other domains, such as wind energy prediction.] A correction has been made to the section [5 CONCLUSION]: "[In the future, we will center on improving the network topology of the GWODESN and apply the model in other domains, such as wind energy prediction and photovoltaic power forecasting.]"
Authors
- Haiyang Zhang (ORCID: https://orcid.org/0000-0002-6565-1950)
- Xiaojuan Chen (ORCID: https://orcid.org/0000-0001-6742-5415)
Institutions
- Changchun University of Science and Technology (CN)
Publication Details
- Journal
- Frontiers in Energy Research
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/fenrg.2026.1770773
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00