Binary neural systems: combining weighted and weightless properties

I. Aleksander, T. Clarke, A. D. P. Braga
{"title":"Binary neural systems: combining weighted and weightless properties","authors":"I. Aleksander, T. Clarke, A. D. P. Braga","doi":"10.1049/ISE.1994.0022","DOIUrl":null,"url":null,"abstract":"A neural function is developed that combines the characteristics of weightless and weighted binary neurons. A new combined generalisation algorithm is presented and applied to a neural state machine which is capable of learning to respond to sequences of inputs. The difficulty with such tasks lies in learning appropriate internal state assignments. A particular ‘iconic’ method of solving this problem is discussed. The analysis includes a discussion of implementational issues.","PeriodicalId":55165,"journal":{"name":"Engineering Intelligent Systems for Electrical Engineering and Communications","volume":"45 1","pages":"211-221"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Intelligent Systems for Electrical Engineering and Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1049/ISE.1994.0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

A neural function is developed that combines the characteristics of weightless and weighted binary neurons. A new combined generalisation algorithm is presented and applied to a neural state machine which is capable of learning to respond to sequences of inputs. The difficulty with such tasks lies in learning appropriate internal state assignments. A particular ‘iconic’ method of solving this problem is discussed. The analysis includes a discussion of implementational issues.
二元神经系统:结合加权和无权重特性
提出了一种结合无权重和加权二值神经元特性的神经函数。提出了一种新的组合泛化算法,并将其应用于能够学习响应输入序列的神经状态机。这类任务的难点在于学习适当的内部状态分配。讨论了解决这一问题的一种特殊的“图示”方法。分析包括对实施问题的讨论。
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