The verification's criterion of learning algorithm

Tymoteusz Bilyk
{"title":"The verification's criterion of learning algorithm","authors":"Tymoteusz Bilyk","doi":"10.1109/ISDA.2005.93","DOIUrl":null,"url":null,"abstract":"Construction of an effective real time learning algorithm is mostly needed for the pulsed neural network nowadays. The learning algorithm should collect and convert the data from networks inputs into a PNN memory while network's working. Keeping of an asynchronous state in the background is a big challenge during learning PNN with a certain number of recurrent connections like Hebbian cell assemblies (HCA) or synfire chains (SFC). This paper presents methods which are usable for refusing or accepting the examined learning algorithm in the relatively short time of simulation and it gives us advice about direction of our research.","PeriodicalId":345842,"journal":{"name":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"5th International Conference on Intelligent Systems Design and Applications (ISDA'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISDA.2005.93","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Construction of an effective real time learning algorithm is mostly needed for the pulsed neural network nowadays. The learning algorithm should collect and convert the data from networks inputs into a PNN memory while network's working. Keeping of an asynchronous state in the background is a big challenge during learning PNN with a certain number of recurrent connections like Hebbian cell assemblies (HCA) or synfire chains (SFC). This paper presents methods which are usable for refusing or accepting the examined learning algorithm in the relatively short time of simulation and it gives us advice about direction of our research.
学习算法的验证准则
目前脉冲神经网络最需要的是构建一种有效的实时学习算法。学习算法需要在网络工作时收集网络输入的数据并将其转换为PNN存储器。在学习具有一定数量的循环连接(如Hebbian cell assemblies (HCA)或synfire chains (SFC))的PNN时,在后台保持异步状态是一个很大的挑战。本文提出了在较短的仿真时间内拒绝或接受被检验的学习算法的方法,并对我们的研究方向提出了建议。
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