为铀萃取工艺组合模型开发人工神经网络

IF 0.4 4区工程技术 Q4 NUCLEAR SCIENCE & TECHNOLOGY Atomic Energy Pub Date : 2024-09-06 DOI:10.1007/s10512-024-01107-6

I. S. Nadezhdin, A. M. Emelyanov, S. N. Liventsov

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引用次数: 0

摘要

根据所进行的文献综述，编制了一个训练样本。为训练人工神经网络选定的最佳参数包括其结构、激活函数、输出层传递函数和隐藏层的神经元数量。使用所开发的人工神经网络进行计算的结果，其不确定性小于 1%，这证明该网络适用于创建技术过程的数字孪生。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Development of an artificial neural network for a combined model of the uranium extraction process

Based on a conducted literature review, a training sample was compiled. The selected optimal parameters for training an artificial neural network included its structure, activation function, output layer transfer function, and the number of neurons in hidden layers. The results of calculations using the developed artificial neural network have an uncertainty of less than 1%, which confirms its suitability for creating a digital twin of a technological process.

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来源期刊

Atomic Energy 工程技术-核科学技术

CiteScore

1.00

自引率

20.00%

发文量

100

审稿时长

4-8 weeks

期刊介绍： Atomic Energy publishes papers and review articles dealing with the latest developments in the peaceful uses of atomic energy. Topics include nuclear chemistry and physics, plasma physics, accelerator characteristics, reactor economics and engineering, applications of isotopes, and radiation monitoring and safety.