Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting
<p>Accurate control of wave energy devices relies heavily on precise forecasts of wave heights, yet the dynamic and fluctuating nature of historical wave data presents significant challenges to achieving this precision. Neural networks help address this issue by extracting meaningful patterns...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513532710617088 |
|---|---|
| author | Aryan Bhambu (18767731) |
| author2 | Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) Natarajan Selvaraju (22631420) |
| author2_role | author author author |
| author_facet | Aryan Bhambu (18767731) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) Natarajan Selvaraju (22631420) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aryan Bhambu (18767731) Ruobin Gao (16003195) Ponnuthurai Nagaratnam Suganthan (11274636) Natarajan Selvaraju (22631420) |
| dc.date.none.fl_str_mv | 2025-11-12T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.asoc.2025.114136 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_random_vector_functional_link_transformer_network_with_multiple_output_layers_for_significant_wave_height_forecasting/30636416 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Forecasting Transformer Multiple output layers Time series Deep ensemble random vector functional vector link Deep neural networks Ensemble learning Randomized neural networks |
| dc.title.none.fl_str_mv | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Accurate control of wave energy devices relies heavily on precise forecasts of wave heights, yet the dynamic and fluctuating nature of historical wave data presents significant challenges to achieving this precision. Neural networks help address this issue by extracting meaningful patterns from past observations to improve wave height predictions. This paper introduces a novel random vector functional link transformer (RFT) and ensemble deep random vector functional link transformer (edRFT) networks to capture the dynamic characteristics of significant wave heights. The model employs hidden blocks based on transformer encoders to effectively capture complex sequential dependencies in the data. The proposed model incorporates randomly initialized and fixed weights for the hidden layers to ensure stability during training. Stacked hidden layers are incorporated to facilitate deep representation learning, allowing the extraction of complex patterns from the data. Forecasts are generated by integrating the outputs of each layer using an ensemble approach. The computational results demonstrate the superiority of the proposed models through a comparative analysis against state-of-the-art approaches across fifteen significant wave height time series, validated by three error metrics and a statistical test.</p><h2>Other Information</h2> <p> Published in: Applied Soft Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.asoc.2025.114136" target="_blank">https://dx.doi.org/10.1016/j.asoc.2025.114136</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_afdb8bb85166b7c0318cb3b9a580bc71 |
| identifier_str_mv | 10.1016/j.asoc.2025.114136 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30636416 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep random vector functional link transformer network with multiple output layers for significant wave height forecastingAryan Bhambu (18767731)Ruobin Gao (16003195)Ponnuthurai Nagaratnam Suganthan (11274636)Natarajan Selvaraju (22631420)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceMachine learningForecastingTransformerMultiple output layersTime seriesDeep ensemble random vector functional vector linkDeep neural networksEnsemble learningRandomized neural networks<p>Accurate control of wave energy devices relies heavily on precise forecasts of wave heights, yet the dynamic and fluctuating nature of historical wave data presents significant challenges to achieving this precision. Neural networks help address this issue by extracting meaningful patterns from past observations to improve wave height predictions. This paper introduces a novel random vector functional link transformer (RFT) and ensemble deep random vector functional link transformer (edRFT) networks to capture the dynamic characteristics of significant wave heights. The model employs hidden blocks based on transformer encoders to effectively capture complex sequential dependencies in the data. The proposed model incorporates randomly initialized and fixed weights for the hidden layers to ensure stability during training. Stacked hidden layers are incorporated to facilitate deep representation learning, allowing the extraction of complex patterns from the data. Forecasts are generated by integrating the outputs of each layer using an ensemble approach. The computational results demonstrate the superiority of the proposed models through a comparative analysis against state-of-the-art approaches across fifteen significant wave height time series, validated by three error metrics and a statistical test.</p><h2>Other Information</h2> <p> Published in: Applied Soft Computing<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.asoc.2025.114136" target="_blank">https://dx.doi.org/10.1016/j.asoc.2025.114136</a></p>2025-11-12T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.asoc.2025.114136https://figshare.com/articles/journal_contribution/Deep_random_vector_functional_link_transformer_network_with_multiple_output_layers_for_significant_wave_height_forecasting/30636416CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306364162025-11-12T15:00:00Z |
| spellingShingle | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting Aryan Bhambu (18767731) Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Forecasting Transformer Multiple output layers Time series Deep ensemble random vector functional vector link Deep neural networks Ensemble learning Randomized neural networks |
| status_str | publishedVersion |
| title | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| title_full | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| title_fullStr | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| title_full_unstemmed | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| title_short | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| title_sort | Deep random vector functional link transformer network with multiple output layers for significant wave height forecasting |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Forecasting Transformer Multiple output layers Time series Deep ensemble random vector functional vector link Deep neural networks Ensemble learning Randomized neural networks |