用描述词和真实词进行计算

I. Turksen
{"title":"用描述词和真实词进行计算","authors":"I. Turksen","doi":"10.1109/NAFIPS.1999.781643","DOIUrl":null,"url":null,"abstract":"Computing with words [Zadeh, 1995] is fundamental to our understanding the foundation of fuzzy theory. Naturally, there are a large variety of \"words\" that we need to recognize and utilize in our computational paradigms. However, descriptive and veristic words are essential for the fundamental derivation of fuzzy set and logic formulas. Descriptive linguistic terms help us assign an element to a fuzzy set with a membership value. Whereas the veristic words determine the degree of truthhood associated with a descriptive membership value. Understanding this distinction between descriptive and veristic linguistic terms form the basis of deriving the normal forms of fuzzy set and logic formulas. Within this perspective, there are potentially four possible fuzzy set and logic theories. Each of these has their own unique formulas. Most of the current literature is based on a \"myopic\" theory which is an approximation of the fuzzy set and two-valued logic based formalism. These issues are presented in our attempt toward restructuring the foundations of fuzzy theory.","PeriodicalId":335957,"journal":{"name":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Computing with descriptive and veristic words\",\"authors\":\"I. Turksen\",\"doi\":\"10.1109/NAFIPS.1999.781643\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computing with words [Zadeh, 1995] is fundamental to our understanding the foundation of fuzzy theory. Naturally, there are a large variety of \\\"words\\\" that we need to recognize and utilize in our computational paradigms. However, descriptive and veristic words are essential for the fundamental derivation of fuzzy set and logic formulas. Descriptive linguistic terms help us assign an element to a fuzzy set with a membership value. Whereas the veristic words determine the degree of truthhood associated with a descriptive membership value. Understanding this distinction between descriptive and veristic linguistic terms form the basis of deriving the normal forms of fuzzy set and logic formulas. Within this perspective, there are potentially four possible fuzzy set and logic theories. Each of these has their own unique formulas. Most of the current literature is based on a \\\"myopic\\\" theory which is an approximation of the fuzzy set and two-valued logic based formalism. These issues are presented in our attempt toward restructuring the foundations of fuzzy theory.\",\"PeriodicalId\":335957,\"journal\":{\"name\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.1999.781643\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.1999.781643","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21

摘要

单词计算[Zadeh, 1995]是我们理解模糊理论基础的基础。当然,在我们的计算范式中,我们需要识别和利用各种各样的“单词”。然而,描述性和真实性词对于模糊集和逻辑公式的基本推导是必不可少的。描述性语言术语帮助我们将元素分配给具有隶属度值的模糊集。而真实词则决定了与描述性隶属值相关联的真实程度。理解描述和真实语言术语之间的区别是推导模糊集和逻辑公式的范式的基础。从这个角度来看,可能有四种可能的模糊集和逻辑理论。每一个都有自己独特的公式。目前大多数文献都是建立在模糊集和二值逻辑的近似形式主义的“近视”理论基础上的。这些问题是在我们试图重构模糊理论的基础时提出的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computing with descriptive and veristic words
Computing with words [Zadeh, 1995] is fundamental to our understanding the foundation of fuzzy theory. Naturally, there are a large variety of "words" that we need to recognize and utilize in our computational paradigms. However, descriptive and veristic words are essential for the fundamental derivation of fuzzy set and logic formulas. Descriptive linguistic terms help us assign an element to a fuzzy set with a membership value. Whereas the veristic words determine the degree of truthhood associated with a descriptive membership value. Understanding this distinction between descriptive and veristic linguistic terms form the basis of deriving the normal forms of fuzzy set and logic formulas. Within this perspective, there are potentially four possible fuzzy set and logic theories. Each of these has their own unique formulas. Most of the current literature is based on a "myopic" theory which is an approximation of the fuzzy set and two-valued logic based formalism. These issues are presented in our attempt toward restructuring the foundations of fuzzy theory.
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
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信