| Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
|---|---|---|
| Article Number | 06018 | |
| Number of page(s) | 8 | |
| Section | Building Energy Modeling | |
| DOI | https://doi.org/10.1051/e3sconf/202458306018 | |
| Published online | 25 October 2024 | |
Comparative analysis of the application of different types of neural networks to the recognition of one-dimensional signals
Don state technical University, 344000, Rostov-on-Don, Gagarin square 1, Russia
* Corresponding author: copybird@yandex.ru
In the processes of determining the properties of materials and structures based on the study of the response to a given dynamic impact, the problem of analysing a one-dimensional time signal and its classification arises. One of the effective approaches to solving it is the use of artificial neural networks with generalized properties of approximation and data filtering. The paper investigates the effectiveness of using fully connected, recurrent and convolutional neural networks to problems of impact indentation to determine the strength properties of metals and elastic moduli of layered structures of non-rigid highways.
© The Authors, published by EDP Sciences, 2024
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