| Autor: |
Anatoliy Semenov, Ekaterina Pecherskaya, Pavel Golubkov, Sergey Gurin, Dmitriy Artamonov, Yuliya Shepeleva |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Heliyon, Vol 9, Iss 9, Pp e19995- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2405-8440 |
| DOI: |
10.1016/j.heliyon.2023.e19995 |
| Popis: |
The article is aimed at solving the problem of parametric identification of non-linear object models using the example of a mathematical model of the micro-arc oxidation process. An algorithm for parametric identification, based on an experiment in the micro-arc oxidation process, the results of which form a training and control sample is proposed; sequential training of neural networks and calculation of the parameters estimates of the nonlinear model according to experimental data are performed. Experimental testing of the proposed method of neural network parametric identification on the example of the micro-arc oxidation process confirmed that the standard deviation of current and voltage from the nominal values does not exceed ±4%. The obtained results were used in the development of an intelligent hardware-software complex for the production of protective coatings by the micro-arc oxidation method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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