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Tehnički vjesnik, Vol. 24 No. 2, 2017.

Izvorni znanstveni članak
https://doi.org/10.17559/TV-20140423164817

Comparison of usage of different neural structures to predict AAO layer thickness

Alena Vagaská ; Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 08001 Prešov, Slovakia
Miroslav Gombár ; Faculty of Management, University of Prešov in Prešov, Konštantínova 16, 08001 Prešov, Slovakia

Puni tekst: engleski, pdf (882 KB) str. 333-339 preuzimanja: 192* citiraj
APA 6th Edition
Vagaská, A. i Gombár, M. (2017). Comparison of usage of different neural structures to predict AAO layer thickness. Tehnički vjesnik, 24 (2), 333-339. https://doi.org/10.17559/TV-20140423164817
MLA 8th Edition
Vagaská, Alena i Miroslav Gombár. "Comparison of usage of different neural structures to predict AAO layer thickness." Tehnički vjesnik, vol. 24, br. 2, 2017, str. 333-339. https://doi.org/10.17559/TV-20140423164817. Citirano 24.03.2019.
Chicago 17th Edition
Vagaská, Alena i Miroslav Gombár. "Comparison of usage of different neural structures to predict AAO layer thickness." Tehnički vjesnik 24, br. 2 (2017): 333-339. https://doi.org/10.17559/TV-20140423164817
Harvard
Vagaská, A., i Gombár, M. (2017). 'Comparison of usage of different neural structures to predict AAO layer thickness', Tehnički vjesnik, 24(2), str. 333-339. doi: https://doi.org/10.17559/TV-20140423164817
Vancouver
Vagaská A, Gombár M. Comparison of usage of different neural structures to predict AAO layer thickness. Tehnički vjesnik [Internet]. 2017 [pristupljeno 24.03.2019.];24(2):333-339. doi: https://doi.org/10.17559/TV-20140423164817
IEEE
A. Vagaská i M. Gombár, "Comparison of usage of different neural structures to predict AAO layer thickness", Tehnički vjesnik, vol.24, br. 2, str. 333-339, 2017. [Online]. doi: https://doi.org/10.17559/TV-20140423164817
Puni tekst: hrvatski, pdf (882 KB) str. 333-339 preuzimanja: 97* citiraj
APA 6th Edition
Vagaská, A. i Gombár, M. (2017). Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida. Tehnički vjesnik, 24 (2), 333-339. https://doi.org/10.17559/TV-20140423164817
MLA 8th Edition
Vagaská, Alena i Miroslav Gombár. "Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida." Tehnički vjesnik, vol. 24, br. 2, 2017, str. 333-339. https://doi.org/10.17559/TV-20140423164817. Citirano 24.03.2019.
Chicago 17th Edition
Vagaská, Alena i Miroslav Gombár. "Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida." Tehnički vjesnik 24, br. 2 (2017): 333-339. https://doi.org/10.17559/TV-20140423164817
Harvard
Vagaská, A., i Gombár, M. (2017). 'Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida', Tehnički vjesnik, 24(2), str. 333-339. doi: https://doi.org/10.17559/TV-20140423164817
Vancouver
Vagaská A, Gombár M. Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida. Tehnički vjesnik [Internet]. 2017 [pristupljeno 24.03.2019.];24(2):333-339. doi: https://doi.org/10.17559/TV-20140423164817
IEEE
A. Vagaská i M. Gombár, "Usporedba primjene raznih neuralnih struktura u predviđanju debljine sloja anodnog aluminij oksida", Tehnički vjesnik, vol.24, br. 2, str. 333-339, 2017. [Online]. doi: https://doi.org/10.17559/TV-20140423164817

Sažetak
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.

Ključne riječi
anodizing; neural unit; prediction model

Hrčak ID: 179839

URI
https://hrcak.srce.hr/179839

[hrvatski]

Posjeta: 568 *