利用人工神经网络预测硼铝层厚度

IF 0.5 Q4 PHYSICS, CONDENSED MATTER
U. L. Mishigdorzhiyn, B. A. Dyshenov, A. P. Semenov, N. S. Ulakhanov, B. E. Markhadayev
{"title":"利用人工神经网络预测硼铝层厚度","authors":"U. L. Mishigdorzhiyn,&nbsp;B. A. Dyshenov,&nbsp;A. P. Semenov,&nbsp;N. S. Ulakhanov,&nbsp;B. E. Markhadayev","doi":"10.1134/S1027451024020344","DOIUrl":null,"url":null,"abstract":"<p>The application of mathematical models and artificial neural networks for predicting the properties of diffusion coatings created by thermal–chemical treatment based on the boroaluminizing process is considered. The formalization and analysis of forecasting experimental results are conducted. Building computer models for prediction based on experimental data of the boroaluminizing process with high accuracy is a solvable task when using artificial neural networks such as a multilayer perceptron. Testing the number of hidden layers and the number of neurons in them revealed the highest correlation coefficient <i>R</i> = 0.99993 for an artificial neural network using two hidden layers with ten and six neurons, respectively. The highest efficiency can be achieved using the hyperbolic tangent activation function.</p>","PeriodicalId":671,"journal":{"name":"Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques","volume":"18 2","pages":"466 - 473"},"PeriodicalIF":0.5000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of the Thickness of a Boroaluminized Layer Using an Artificial Neural Network\",\"authors\":\"U. L. Mishigdorzhiyn,&nbsp;B. A. Dyshenov,&nbsp;A. P. Semenov,&nbsp;N. S. Ulakhanov,&nbsp;B. E. Markhadayev\",\"doi\":\"10.1134/S1027451024020344\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The application of mathematical models and artificial neural networks for predicting the properties of diffusion coatings created by thermal–chemical treatment based on the boroaluminizing process is considered. The formalization and analysis of forecasting experimental results are conducted. Building computer models for prediction based on experimental data of the boroaluminizing process with high accuracy is a solvable task when using artificial neural networks such as a multilayer perceptron. Testing the number of hidden layers and the number of neurons in them revealed the highest correlation coefficient <i>R</i> = 0.99993 for an artificial neural network using two hidden layers with ten and six neurons, respectively. The highest efficiency can be achieved using the hyperbolic tangent activation function.</p>\",\"PeriodicalId\":671,\"journal\":{\"name\":\"Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques\",\"volume\":\"18 2\",\"pages\":\"466 - 473\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1027451024020344\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHYSICS, CONDENSED MATTER\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1134/S1027451024020344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, CONDENSED MATTER","Score":null,"Total":0}
引用次数: 0

摘要

摘要 本研究考虑了数学模型和人工神经网络在预测基于硼铝化工艺的热化学处理扩散涂层性能方面的应用。对预测实验结果进行了形式化和分析。使用多层感知器等人工神经网络,可以根据硼铝化工艺的实验数据建立高精度预测计算机模型。对隐藏层数和其中的神经元数量进行测试后发现,使用分别有 10 个和 6 个神经元的两个隐藏层的人工神经网络的相关系数 R = 0.99993 最高。使用双曲正切激活函数的效率最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction of the Thickness of a Boroaluminized Layer Using an Artificial Neural Network

Prediction of the Thickness of a Boroaluminized Layer Using an Artificial Neural Network

Prediction of the Thickness of a Boroaluminized Layer Using an Artificial Neural Network

The application of mathematical models and artificial neural networks for predicting the properties of diffusion coatings created by thermal–chemical treatment based on the boroaluminizing process is considered. The formalization and analysis of forecasting experimental results are conducted. Building computer models for prediction based on experimental data of the boroaluminizing process with high accuracy is a solvable task when using artificial neural networks such as a multilayer perceptron. Testing the number of hidden layers and the number of neurons in them revealed the highest correlation coefficient R = 0.99993 for an artificial neural network using two hidden layers with ten and six neurons, respectively. The highest efficiency can be achieved using the hyperbolic tangent activation function.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
0.90
自引率
25.00%
发文量
144
审稿时长
3-8 weeks
期刊介绍: Journal of Surface Investigation: X-ray, Synchrotron and Neutron Techniques publishes original articles on the topical problems of solid-state physics, materials science, experimental techniques, condensed media, nanostructures, surfaces of thin films, and phase boundaries: geometric and energetical structures of surfaces, the methods of computer simulations; physical and chemical properties and their changes upon radiation and other treatments; the methods of studies of films and surface layers of crystals (XRD, XPS, synchrotron radiation, neutron and electron diffraction, electron microscopic, scanning tunneling microscopic, atomic force microscopic studies, and other methods that provide data on the surfaces and thin films). Articles related to the methods and technics of structure studies are the focus of the journal. The journal accepts manuscripts of regular articles and reviews in English or Russian language from authors of all countries. All manuscripts are peer-reviewed.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信