Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F22%3A10250032" target="_blank" >RIV/61989100:27360/22:10250032 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000861662000001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000861662000001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.matdes.2022.110880" target="_blank" >10.1016/j.matdes.2022.110880</a>
Alternative languages
Result language
angličtina
Original language name
Shallow and deep learning of an artificial neural network model describing a hot flow stress Evolution: A comparative study
Original language description
In recent years, the utilization of artificial neural networks (ANNs) as regression models to solve the issue of hot flow stress forecasting has become a standard approach. In a connection with this kind of regression issue, employed ANNs are usually learned via a shallow learning technique while only limited attention has been paid to a deep learning method. In the frame of the submitted research, the shallow learning approach is thoroughly compared to the deep learning techniques which are based on the use of a Restricted Boltzmann Machine (RBM) and an Auto-Encoder (AE). To do so, these learning techniques are applied on a feed-forward multi-layer ANN describing the experimental hot flow curve dataset of micro-alloyed medium carbon steel. In comparison with the shallow learning method, both deep learning approaches provided higher accuracy in the network response - especially in the case of a higher number of hidden layers. The results have also shown that neither the RBM-based deep learning method nor the AE-based method had a significant effect on the duration of the necessary calculations. However, it turned out that the RBM-based method can, under certain conditions, lead to a more reliable network performance. (C) 2022 The Author(s). Published by Elsevier Ltd.
Czech name
—
Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20500 - Materials engineering
Result continuities
Project
<a href="/en/project/EF17_049%2F0008399" target="_blank" >EF17_049/0008399: Development of inter-sector cooperation of RMSTC with the application sphere in the field of advanced research and innovations of classical metal materials and technologies using modelling methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Materials and Design
ISSN
0264-1275
e-ISSN
1873-4197
Volume of the periodical
220
Issue of the periodical within the volume
110880
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
nestrankovano
UT code for WoS article
000861662000001
EID of the result in the Scopus database
2-s2.0-85132875961