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
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
Datum izdavanja:
14.4.2017.
Posjeta: 1.883 *