修正的邓氏面板数据灰色关系分析模型及其在鄱阳湖水环境评估中的应用

IF 2.8 4区 工程技术 Q2 ENGINEERING, CHEMICAL Processes Pub Date : 2024-09-09 DOI:10.3390/pr12091935
Fanghong Jian, Jiangfeng Li, Xiaomei Liu, Qiong Wu, Dan Zhong
{"title":"修正的邓氏面板数据灰色关系分析模型及其在鄱阳湖水环境评估中的应用","authors":"Fanghong Jian, Jiangfeng Li, Xiaomei Liu, Qiong Wu, Dan Zhong","doi":"10.3390/pr12091935","DOIUrl":null,"url":null,"abstract":"Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension.","PeriodicalId":20597,"journal":{"name":"Processes","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modified Deng’s Grey Relational Analysis Model for Panel Data and Its Applications in Assessing the Water Environment of Poyang Lake\",\"authors\":\"Fanghong Jian, Jiangfeng Li, Xiaomei Liu, Qiong Wu, Dan Zhong\",\"doi\":\"10.3390/pr12091935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension.\",\"PeriodicalId\":20597,\"journal\":{\"name\":\"Processes\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Processes\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/pr12091935\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Processes","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/pr12091935","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

摘要

邓氏灰色关系分析(GRA)模型因其简单的数学机制而被广泛应用于聚类分析。针对不同维度的样本数据,人们提出了不同的邓氏 GRA 模型,包括时间序列数据、面板数据和面板时间序列数据。本文旨在改进现有 Deng's GRA 模型对面板数据的聚类精度,以克服其存在的一些不足。首先,以机器人执行故障数据集 LP1 为基础,对现有的面板数据 Deng's GRA 模型进行了测试。然后,根据测试结果,利用泰勒公式对现有的 Deng 面板数据 GRA 模型进行修正,并通过机器人执行故障数据集 LP1 成功验证了修正后的模型。最后,在实际应用中,将改进后的面板数据邓氏 GRA 模型用于评估鄱阳湖近五年的水环境状况。与其他聚类方法相比,案例研究结果表明,修正的邓氏面板数据 GRA 模型是适用的,同时也证实了中国政府对鄱阳湖水质监管的显著成效。因此,本文提出的改进型邓氏 GRA 模型与原始模型相比提高了聚类精度,可以很好地应用于大维度数据的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Modified Deng’s Grey Relational Analysis Model for Panel Data and Its Applications in Assessing the Water Environment of Poyang Lake
Deng’s grey relational analysis (GRA) model is widely used in clustering because of its simple mathematical mechanisms. For sample data of different dimensions, people have put forward different Deng’s GRA models, including time series data, panel data, and panel time series data. The purpose of this paper is to improve the clustering accuracy of the existing Deng’s GRA model for panel data in order to overcome some of its shortcomings. Firstly, the existing Deng’s GRA model for panel data was tested based on the dataset LP1 of Robot Execution Failures. Then, according to the test results, the existing Deng’s GRA model for panel data is modified by means of Taylor’s formula, and the modified model is successfully validated by the dataset LP1 of Robot Execution Failures. Finally, as a practical application, the modified Deng’s GRA model for panel data is applied to assess the water environment of Poyang Lake over the past five years. Compared with other cluster methods, the results of the case study show that the modified Deng’s GRA model for panel data is applicable and also confirm the remarkable effectiveness of the Chinese government’s water quality regulation in Poyang Lake. Therefore, the modified Deng’s GRA model presented in this paper improves the clustering accuracy compared to the original model and can be applied well to the classification of data with a large dimension.
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
Processes
Processes Chemical Engineering-Bioengineering
CiteScore
5.10
自引率
11.40%
发文量
2239
审稿时长
14.11 days
期刊介绍: Processes (ISSN 2227-9717) provides an advanced forum for process related research in chemistry, biology and allied engineering fields. The journal publishes regular research papers, communications, letters, short notes and reviews. Our aim is to encourage researchers to publish their experimental, theoretical and computational results in as much detail as necessary. There is no restriction on paper length or number of figures and tables.
期刊最新文献
Evaluation of Various Drying Methods for Mexican Yahualica chili: Drying Characteristics and Quality Assessment Innovative and Patented Liposome-Based Drug Carriers Understanding Perforation Detonation Failure Mechanisms Based on Physicochemical Detection and Simulation Modeling Lead Ion Adsorption on Glutathione-Modified Carbon Finite Element Analysis of Laminar Natural Convection in a Differentially Heated Porous Cavity Using the Darcy–Brinkman Model
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1