递归神经网络与s(s/spl ges/2)阶Fibonacci计数系统

M. Yacoub
{"title":"递归神经网络与s(s/spl ges/2)阶Fibonacci计数系统","authors":"M. Yacoub","doi":"10.1109/ICNN.1994.374510","DOIUrl":null,"url":null,"abstract":"In the Fibonacci numeration system of order s(s/spl ges/2), every positive integer admits a unique representation which does not contain s consecutive digits equal to 1 (called normal form). We show how this normal form can be obtained from any representation by recurrent neural networks. The addition of two integers in this system and the conversion from a Fibonacci representation to a standard binary representation (and conversely) can also be realized using recurrent neural networks.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recurrent neural networks and Fibonacci numeration system of order s(s/spl ges/2)\",\"authors\":\"M. Yacoub\",\"doi\":\"10.1109/ICNN.1994.374510\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the Fibonacci numeration system of order s(s/spl ges/2), every positive integer admits a unique representation which does not contain s consecutive digits equal to 1 (called normal form). We show how this normal form can be obtained from any representation by recurrent neural networks. The addition of two integers in this system and the conversion from a Fibonacci representation to a standard binary representation (and conversely) can also be realized using recurrent neural networks.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374510\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

在s(s/spl ges/2)阶的斐波那契数制中,每一个正整数都有一个不包含等于1的5个连续数字的唯一表示(称为范式)。我们展示了如何通过递归神经网络从任何表示中获得这种范式。在这个系统中,两个整数的加法和从斐波那契表示到标准二进制表示(或反过来)的转换也可以用递归神经网络来实现
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recurrent neural networks and Fibonacci numeration system of order s(s/spl ges/2)
In the Fibonacci numeration system of order s(s/spl ges/2), every positive integer admits a unique representation which does not contain s consecutive digits equal to 1 (called normal form). We show how this normal form can be obtained from any representation by recurrent neural networks. The addition of two integers in this system and the conversion from a Fibonacci representation to a standard binary representation (and conversely) can also be realized using recurrent neural networks.<>
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:481959085
Book学术官方微信