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

Rad se bavi usporedbom uporabe triju osnovnih vrsta neuralnih jedinica u cilju stvaranja najprikladnijeg modela koji predviđa utvrđivanje konačne debljine sloja aluminijeva oksida koji nastaje na površini uz gustoću struje od 1 A∙dm−2. Osim toga, prati se pouzdanost dobivenih modela predviđanja, ovisno o količini podataka za vježbu. Uz pravilno odabrani raspon podataka za vježbu moguće je stvoriti modele predviđanja s pouzdanošću većom od 95 % s postignutom tolerancijom 2×10−6 mm.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