Prognostification of Specific Steel Resistivity for Modeling Electrothermal Processes

机译：用于模拟电热过程的比钢电阻率的预后

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The analysis of the data approximation methods was carried out in the paper. The method of approximation using neural networks was chosen. A neural network was created to predict steel resistivity. For the created neural network, the training method was chosen. After the training, the results were analyzed.

机译：本文对数据近似方法进行了分析。选择了使用神经网络的近似方法。创建了一个神经网络来预测钢的电阻率。对于创建的神经网络，选择了训练方法。训练后，分析结果。

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来源
《International Conference Complex Systems: Control and Modeling Problems》|2019年|370-373|共4页
会议地点 Samara(RU)
作者
Aleksei Bondar; Olga Streltsova; Fedor Chmilenko; Dmitriy Bondarenko;
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作者单位

Saint-Petersburg Electrotechnical University “LETI” St. Petersburg Russia;

Saint-Petersburg Electrotechni;

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原文格式 PDF
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关键词
approximation theory; materials science computing; neural nets; steel;

机译：近似理论材料科学计算；神经网络钢;

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4. Prognostification of Specific Steel Resistivity for Modeling Electrothermal Processes [C] . Aleksei Bondar, Olga Streltsova, Fedor Chmilenko, International Conference Complex Systems: Control and Modeling Problems . 2019

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Prognostification of Specific Steel Resistivity for Modeling Electrothermal Processes

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