Finite iteration DT-CNN - new design and operating principles

C. Merkwirth, Jochen Bröcker, M. Ogorzałek, J. Wichard
{"title":"Finite iteration DT-CNN - new design and operating principles","authors":"C. Merkwirth, Jochen Bröcker, M. Ogorzałek, J. Wichard","doi":"10.1109/ISCAS.2004.1329696","DOIUrl":null,"url":null,"abstract":"In this paper we propose to use the discrete-time cellular neural network (DT-CNN) in a finite iterate mode. In such a mode of operation no special requirements on template stability properties are needed. We propose a constructive back propagation based algorithm for template design. For a given number of iterations we can find optimal sequence of templates for a given problem to be solved. Our novel approach is demonstrated by a design of a digit recognition DT-CNN.","PeriodicalId":6445,"journal":{"name":"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)","volume":"1 1","pages":"V-V"},"PeriodicalIF":0.0000,"publicationDate":"2004-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCAS.2004.1329696","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

In this paper we propose to use the discrete-time cellular neural network (DT-CNN) in a finite iterate mode. In such a mode of operation no special requirements on template stability properties are needed. We propose a constructive back propagation based algorithm for template design. For a given number of iterations we can find optimal sequence of templates for a given problem to be solved. Our novel approach is demonstrated by a design of a digit recognition DT-CNN.
有限迭代DT-CNN -新的设计和工作原理
本文提出在有限迭代模式下使用离散时间细胞神经网络(DT-CNN)。在这种操作模式下,不需要对模板的稳定性有特殊要求。提出了一种基于建设性反向传播的模板设计算法。对于给定的迭代次数,我们可以为要解决的给定问题找到最优的模板序列。我们的新方法通过数字识别DT-CNN的设计来证明。
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