{"title":"Fuzzy logic: issues, contentions and perspectives","authors":"L. Zadeh","doi":"10.1109/ICASSP.1994.389912","DOIUrl":null,"url":null,"abstract":"There has been a rapid growth in the number and variety of applications of fuzzy logic. The successes of fuzzy logic have also generated a skeptical reaction. Most of the criticisms directed at fuzzy logic are rooted in a misunderstanding of what it is and/or a lack of familiarity with it. In many cases, what is not recognized is that the term fuzzy logic (FL) is actually used in two different senses. In a narrow sense, fuzzy logic (FLn) is a logical system which is an extension of multivalued logic and is intended to serve as a logic of approximate reasoning. But in a wider sense, fuzzy logic (FLw) is more or less synonymous with the theory of fuzzy sets (FST). Today the term fuzzy logic is used predominantly in its wider sense. It is in this sense that any field X can be fuzzified-and hence generalized by replacing the concept of a crisp set in X by a fuzzy set. What is gained through fuzzification is greater generality, higher expressive power, an enhanced ability to model real-world phenomena and a methodology for exploiting the tolerance for imprecision. Most of the applications of fuzzy logic relate to control in the context of industrial systems and consumer products. What is discernible, however, is (a) the trend toward the use of fuzzy logic in task-oriented-rather than set-point-oriented-control; and (b) the incorporation of fuzzy logic and neural network techniques in the conception and design of complex systems in which control and expert system techniques are used in combination.<<ETX>>","PeriodicalId":290798,"journal":{"name":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"31 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"48","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICASSP.1994.389912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 48

Abstract

There has been a rapid growth in the number and variety of applications of fuzzy logic. The successes of fuzzy logic have also generated a skeptical reaction. Most of the criticisms directed at fuzzy logic are rooted in a misunderstanding of what it is and/or a lack of familiarity with it. In many cases, what is not recognized is that the term fuzzy logic (FL) is actually used in two different senses. In a narrow sense, fuzzy logic (FLn) is a logical system which is an extension of multivalued logic and is intended to serve as a logic of approximate reasoning. But in a wider sense, fuzzy logic (FLw) is more or less synonymous with the theory of fuzzy sets (FST). Today the term fuzzy logic is used predominantly in its wider sense. It is in this sense that any field X can be fuzzified-and hence generalized by replacing the concept of a crisp set in X by a fuzzy set. What is gained through fuzzification is greater generality, higher expressive power, an enhanced ability to model real-world phenomena and a methodology for exploiting the tolerance for imprecision. Most of the applications of fuzzy logic relate to control in the context of industrial systems and consumer products. What is discernible, however, is (a) the trend toward the use of fuzzy logic in task-oriented-rather than set-point-oriented-control; and (b) the incorporation of fuzzy logic and neural network techniques in the conception and design of complex systems in which control and expert system techniques are used in combination.<>
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
模糊逻辑:问题、争论和观点
模糊逻辑的应用在数量和种类上都有了快速的增长。模糊逻辑的成功也产生了怀疑的反应。大多数针对模糊逻辑的批评都源于对它的误解和/或对它缺乏熟悉。在许多情况下,没有被认识到的是,术语模糊逻辑(FL)实际上有两种不同的含义。从狭义上讲,模糊逻辑是多值逻辑的扩展,是一种近似推理的逻辑系统。但在更广泛的意义上,模糊逻辑(FLw)或多或少是模糊集理论(FST)的同义词。今天,模糊逻辑这个术语主要是在更广泛的意义上使用的。在这个意义上,任何域X都可以被模糊化——因此可以通过用模糊集代替X中的清晰集的概念来推广。通过模糊化获得的是更大的通用性、更高的表达能力、对现实世界现象建模的增强能力,以及一种利用不精确容忍度的方法。模糊逻辑的大多数应用涉及工业系统和消费产品中的控制。然而,可以看出的是:(a)在面向任务的控制中使用模糊逻辑的趋势,而不是面向设定点的控制;(b)将模糊逻辑和神经网络技术结合到控制和专家系统技术相结合的复杂系统的概念和设计中
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A new cumulant based parameter estimation method for noncausal autoregressive systems Using Gaussian mixture modeling in speech recognition An evaluation of cross-language adaptation for rapid HMM development in a new language Unsupervised segmentation of radar images using wavelet decomposition and cumulants Improving speech recognition performance via phone-dependent VQ codebooks and adaptive language models in SPHINX-II
×
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