误差反向传播算法在高校学生学籍管理中的应用研究

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
XinXiu Yang
{"title":"误差反向传播算法在高校学生学籍管理中的应用研究","authors":"XinXiu Yang","doi":"10.4018/ijicte.348960","DOIUrl":null,"url":null,"abstract":"The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" 20","pages":""},"PeriodicalIF":16.4000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions\",\"authors\":\"XinXiu Yang\",\"doi\":\"10.4018/ijicte.348960\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":\" 20\",\"pages\":\"\"},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2024-07-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijicte.348960\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijicte.348960","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

这项工作的目的是根据高校学生状态管理(SSM)中的信息预测学生的就业率。首先,介绍了 SSM 的相关内容。其次,介绍了 BP(反向传播)神经网络、LM(莱文伯格-马夸特)算法和 BR(贝叶斯正则化)算法。此外,将 LM 算法与 BR 算法相结合,对 BP 神经网络进行优化,从而建立基于 LM-BP 神经网络的高校毕业生就业率预测模型(已建立的预测模型)。最后,利用大数据分析技术对 SSM 中大学生的历史数据进行管理和处理,验证了所建立的预测模型。结果表明,所建立的预测模型具有更高的预测精度、更稳定的预测性能、更理想的预测效果和更高的实际应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on the Application of Error Back-Propagation Algorithm Applied to the Student Status Management in Higher Education Institutions
The objective of this work is to predict the employment rate of students based on the information in the SSM (student status management) in colleges and universities. Firstly, the relevant content of SSM is introduced. Secondly, the BP (Back Propagation) neural network, the LM (Levenberg Marquardt) algorithm, and the BR (Bayesian Regularization) algorithm are introduced. In addition, the LM algorithm is combined with the BR algorithm to optimize the BP neural network, so as to establish a prediction model of the employment rate of college graduates based on the LM-BP neural network (the established prediction model). Finally, the established prediction model is verified after the historical data of college students in the SSM are managed and processed using the big data analysis technology. It suggests that the established prediction model shows higher prediction accuracy, more stable prediction performance, more ideal prediction effect, and higher practical application value.
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
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
×
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