GM(1,1)模型中背景值的优化

IF 1 4区 工程技术 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Zhengxin Wang, Yao-guo Dang, Sifeng Liu, Jing Zhou
{"title":"GM(1,1)模型中背景值的优化","authors":"Zhengxin Wang, Yao-guo Dang, Sifeng Liu, Jing Zhou","doi":"10.30016/JGS.200709.0002","DOIUrl":null,"url":null,"abstract":"In this paper, we prove that discrete function with non-homogeneous exponential law is generated by accumulating the discrete function with homogeneous exponential law while discrete function with homogeneous exponential law is generated by inversely-accumulating the discrete function with non-homogeneous exponential law. Based on the error analysis of the Model GM(1,1), we use the discrete function with non-homogeneous exponential law to fit the accumulated sequence in order to propose a new method for optimizing background value in Model GM(1,1). By contrasting the optimum model to the GM one with the simulation, it can be concluded that the new model broke through the restricts of adaption coefficient and it still improved its matching and prediction precision.","PeriodicalId":50187,"journal":{"name":"Journal of Grey System","volume":"10 1","pages":"69-74"},"PeriodicalIF":1.0000,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"The Optimization of Background Value in GM(1,1) Model\",\"authors\":\"Zhengxin Wang, Yao-guo Dang, Sifeng Liu, Jing Zhou\",\"doi\":\"10.30016/JGS.200709.0002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we prove that discrete function with non-homogeneous exponential law is generated by accumulating the discrete function with homogeneous exponential law while discrete function with homogeneous exponential law is generated by inversely-accumulating the discrete function with non-homogeneous exponential law. Based on the error analysis of the Model GM(1,1), we use the discrete function with non-homogeneous exponential law to fit the accumulated sequence in order to propose a new method for optimizing background value in Model GM(1,1). By contrasting the optimum model to the GM one with the simulation, it can be concluded that the new model broke through the restricts of adaption coefficient and it still improved its matching and prediction precision.\",\"PeriodicalId\":50187,\"journal\":{\"name\":\"Journal of Grey System\",\"volume\":\"10 1\",\"pages\":\"69-74\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2007-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Grey System\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.30016/JGS.200709.0002\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Grey System","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.30016/JGS.200709.0002","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 25

摘要

本文证明了具有非齐次指数律的离散函数是由具有齐次指数律的离散函数累加得到的,而具有齐次指数律的离散函数是由具有非齐次指数律的离散函数反累加得到的。在分析GM(1,1)模型误差的基础上,利用非齐次指数律离散函数拟合累积序列,提出了GM(1,1)模型背景值优化的新方法。将最优模型与GM模型进行仿真对比,表明新模型突破了自适应系数的限制,并提高了模型的匹配精度和预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Optimization of Background Value in GM(1,1) Model
In this paper, we prove that discrete function with non-homogeneous exponential law is generated by accumulating the discrete function with homogeneous exponential law while discrete function with homogeneous exponential law is generated by inversely-accumulating the discrete function with non-homogeneous exponential law. Based on the error analysis of the Model GM(1,1), we use the discrete function with non-homogeneous exponential law to fit the accumulated sequence in order to propose a new method for optimizing background value in Model GM(1,1). By contrasting the optimum model to the GM one with the simulation, it can be concluded that the new model broke through the restricts of adaption coefficient and it still improved its matching and prediction precision.
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
Journal of Grey System
Journal of Grey System 数学-数学跨学科应用
CiteScore
2.40
自引率
43.80%
发文量
0
审稿时长
1.5 months
期刊介绍: The journal is a forum of the highest professional quality for both scientists and practitioners to exchange ideas and publish new discoveries on a vast array of topics and issues in grey system. It aims to bring forth anything from either innovative to known theories or practical applications in grey system. It provides everyone opportunities to present, criticize, and discuss their findings and ideas with others. A number of areas of particular interest (but not limited) are listed as follows: Grey mathematics- Generator of Grey Sequences- Grey Incidence Analysis Models- Grey Clustering Evaluation Models- Grey Prediction Models- Grey Decision Making Models- Grey Programming Models- Grey Input and Output Models- Grey Control- Grey Game- Practical Applications.
×
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