Comparison of usage of different neural structures to predict AAO layer thickness
The paper deals with the comparison of usage of three basic types of neural units in order to create the most suitable model predicting determining the final thickness of the alumina layer formed at surface with current density of 1 A∙dm−2. In addition, the reliability of obtained prediction models,...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2017-01-01
|
| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/265142 |
| _version_ | 1797207891018514432 |
|---|---|
| author | Alena Vagaská Miroslav Gombár |
| author_facet | Alena Vagaská Miroslav Gombár |
| author_sort | Alena Vagaská |
| collection | DOAJ |
| description | The paper deals with the comparison of usage of three basic types of neural units in order to create the most suitable model predicting determining the final thickness of the alumina layer formed at surface with current density of 1 A∙dm−2. In addition, the reliability of obtained prediction models, depending on the amount of training data, has been monitored. With properly selected range of training data it is possible to create prediction models with reliability greater than 95 % with achieved toleration 2×10−6 mm. |
| first_indexed | 2024-04-24T09:30:06Z |
| format | Article |
| id | doaj.art-a1016d090b9a41a5b7fe98f559ab9c9a |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:30:06Z |
| publishDate | 2017-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-a1016d090b9a41a5b7fe98f559ab9c9a2024-04-15T14:08:09ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392017-01-0124233333910.17559/TV-20140423164817Comparison of usage of different neural structures to predict AAO layer thicknessAlena Vagaská0Miroslav Gombár1Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 08001 Prešov, SlovakiaFaculty of Management, University of Prešov in Prešov, Konštantínova 16, 08001 Prešov, SlovakiaThe paper deals with the comparison of usage of three basic types of neural units in order to create the most suitable model predicting determining the final thickness of the alumina layer formed at surface with current density of 1 A∙dm−2. In addition, the reliability of obtained prediction models, depending on the amount of training data, has been monitored. With properly selected range of training data it is possible to create prediction models with reliability greater than 95 % with achieved toleration 2×10−6 mm.https://hrcak.srce.hr/file/265142anodizingneural unitprediction model |
| spellingShingle | Alena Vagaská Miroslav Gombár Comparison of usage of different neural structures to predict AAO layer thickness Tehnički Vjesnik anodizing neural unit prediction model |
| title | Comparison of usage of different neural structures to predict AAO layer thickness |
| title_full | Comparison of usage of different neural structures to predict AAO layer thickness |
| title_fullStr | Comparison of usage of different neural structures to predict AAO layer thickness |
| title_full_unstemmed | Comparison of usage of different neural structures to predict AAO layer thickness |
| title_short | Comparison of usage of different neural structures to predict AAO layer thickness |
| title_sort | comparison of usage of different neural structures to predict aao layer thickness |
| topic | anodizing neural unit prediction model |
| url | https://hrcak.srce.hr/file/265142 |
| work_keys_str_mv | AT alenavagaska comparisonofusageofdifferentneuralstructurestopredictaaolayerthickness AT miroslavgombar comparisonofusageofdifferentneuralstructurestopredictaaolayerthickness |