Fuzzy CMAC structures

Kamran Mohajeri, M. Zakizadeh, B. Moaveni, M. Teshnehlab
{"title":"Fuzzy CMAC structures","authors":"Kamran Mohajeri, M. Zakizadeh, B. Moaveni, M. Teshnehlab","doi":"10.1109/FUZZY.2009.5277185","DOIUrl":null,"url":null,"abstract":"Cerebellum Model Articulation Controller (CMAC) is known as a feedforward Neural Network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2009.5277185","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

Abstract

Cerebellum Model Articulation Controller (CMAC) is known as a feedforward Neural Network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.
模糊CMAC结构
小脑模型发音控制器(CMAC)是一种具有快速学习和高性能的前馈神经网络。对其进行了许多改进,其中模糊CMAC (FCMAC)是最重要的改进之一。模糊CMAC作为一种神经模糊系统,提高了精度,减小了内存大小,使CMAC具有可微性。此外,FCMAC将CMAC神经网络从黑盒转换为白盒,其操作可以使用模糊规则解释。模糊CMAC在文献中没有一个独特的结构,在隶属函数、记忆分层结构、去模糊化和模糊系统应用等方面存在差异。在此基础上,对文献中模糊CMAC的不同结构进行了综述。
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 求助全文
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:481959085
Book学术官方微信