Comparison of proposed method with other existing methods.
<p>Comparison of proposed method with other existing methods.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852025907531743232 |
|---|---|
| author | Junaid Khan (4276162) |
| author2 | Umar Zaman (19859645) Eunkyu Lee (5967341) Awatef Salim Balobaid (19859648) R. Y. Aburasain (19859651) Muhammad Bilal (737265) Kyungsup Kim (19859654) |
| author2_role | author author author author author author |
| author_facet | Junaid Khan (4276162) Umar Zaman (19859645) Eunkyu Lee (5967341) Awatef Salim Balobaid (19859648) R. Y. Aburasain (19859651) Muhammad Bilal (737265) Kyungsup Kim (19859654) |
| author_role | author |
| dc.creator.none.fl_str_mv | Junaid Khan (4276162) Umar Zaman (19859645) Eunkyu Lee (5967341) Awatef Salim Balobaid (19859648) R. Y. Aburasain (19859651) Muhammad Bilal (737265) Kyungsup Kim (19859654) |
| dc.date.none.fl_str_mv | 2024-10-16T17:27:01Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0311734.g014 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Comparison_of_proposed_method_with_other_existing_methods_/27243362 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified advanced models using >&# 946 ;</ >&# 945 ;</ neural networks within integrating neural networks neural network outputs incorporate neural network driven parameter tuning dynamic system analysis based prediction models beta filter demonstrates filtering algorithms stands div >< p kalman filter employs neural network integration based kalman filter optimizing prediction accuracy neural network beta filter filtering algorithms kalman filter prediction capabilities prediction accuracy internal parameter dynamic systems dynamic augmentation based alpha widely recognized significant 38 r </ often limited novel approach noise factor f </ effective strategy conventional filters changing conditions cannot adapt approach involves |
| dc.title.none.fl_str_mv | Comparison of proposed method with other existing methods. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>Comparison of proposed method with other existing methods.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_faf723b7cfd2ad369663ecb045b36fd2 |
| identifier_str_mv | 10.1371/journal.pone.0311734.g014 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27243362 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of proposed method with other existing methods.Junaid Khan (4276162)Umar Zaman (19859645)Eunkyu Lee (5967341)Awatef Salim Balobaid (19859648)R. Y. Aburasain (19859651)Muhammad Bilal (737265)Kyungsup Kim (19859654)BiotechnologySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedadvanced models using>&# 946 ;</>&# 945 ;</neural networks withinintegrating neural networksneural network outputsincorporate neural networkdriven parameter tuningdynamic system analysisbased prediction modelsbeta filter demonstratesfiltering algorithms standsdiv >< pkalman filter employsneural network integrationbased kalman filteroptimizing prediction accuracyneural networkbeta filterfiltering algorithmskalman filterprediction capabilitiesprediction accuracyinternal parameterdynamic systemsdynamic augmentationbased alphawidely recognizedsignificant 38r </often limitednovel approachnoise factorf </effective strategyconventional filterschanging conditionscannot adaptapproach involves<p>Comparison of proposed method with other existing methods.</p>2024-10-16T17:27:01ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0311734.g014https://figshare.com/articles/figure/Comparison_of_proposed_method_with_other_existing_methods_/27243362CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272433622024-10-16T17:27:01Z |
| spellingShingle | Comparison of proposed method with other existing methods. Junaid Khan (4276162) Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified advanced models using >&# 946 ;</ >&# 945 ;</ neural networks within integrating neural networks neural network outputs incorporate neural network driven parameter tuning dynamic system analysis based prediction models beta filter demonstrates filtering algorithms stands div >< p kalman filter employs neural network integration based kalman filter optimizing prediction accuracy neural network beta filter filtering algorithms kalman filter prediction capabilities prediction accuracy internal parameter dynamic systems dynamic augmentation based alpha widely recognized significant 38 r </ often limited novel approach noise factor f </ effective strategy conventional filters changing conditions cannot adapt approach involves |
| status_str | publishedVersion |
| title | Comparison of proposed method with other existing methods. |
| title_full | Comparison of proposed method with other existing methods. |
| title_fullStr | Comparison of proposed method with other existing methods. |
| title_full_unstemmed | Comparison of proposed method with other existing methods. |
| title_short | Comparison of proposed method with other existing methods. |
| title_sort | Comparison of proposed method with other existing methods. |
| topic | Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified advanced models using >&# 946 ;</ >&# 945 ;</ neural networks within integrating neural networks neural network outputs incorporate neural network driven parameter tuning dynamic system analysis based prediction models beta filter demonstrates filtering algorithms stands div >< p kalman filter employs neural network integration based kalman filter optimizing prediction accuracy neural network beta filter filtering algorithms kalman filter prediction capabilities prediction accuracy internal parameter dynamic systems dynamic augmentation based alpha widely recognized significant 38 r </ often limited novel approach noise factor f </ effective strategy conventional filters changing conditions cannot adapt approach involves |