一种新的自适应自组织网络

S. Kawahara, T. Saito
{"title":"一种新的自适应自组织网络","authors":"S. Kawahara, T. Saito","doi":"10.1109/CNNA.1996.566487","DOIUrl":null,"url":null,"abstract":"In this paper a new algorithm is presented in order to overcome the stability vs. formation ability dilemma of competitive learning. This algorithm is based on growing cell structures of self-organizing mapping. The new algorithm is effective for endless learning and automatic classification. Applying the algorithm in the case where the input pattern is changed temporally, we have confirmed that it has much better performance than conventional algorithms.","PeriodicalId":222524,"journal":{"name":"1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1996-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"On a novel adaptive self organizing network\",\"authors\":\"S. Kawahara, T. Saito\",\"doi\":\"10.1109/CNNA.1996.566487\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper a new algorithm is presented in order to overcome the stability vs. formation ability dilemma of competitive learning. This algorithm is based on growing cell structures of self-organizing mapping. The new algorithm is effective for endless learning and automatic classification. Applying the algorithm in the case where the input pattern is changed temporally, we have confirmed that it has much better performance than conventional algorithms.\",\"PeriodicalId\":222524,\"journal\":{\"name\":\"1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1996.566487\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1996 Fourth IEEE International Workshop on Cellular Neural Networks and their Applications Proceedings (CNNA-96)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1996.566487","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

本文提出了一种新的算法来克服竞争学习的稳定性和形成能力困境。该算法基于生长细胞结构的自组织映射。该算法具有无限学习和自动分类的优点。将该算法应用于输入模式临时改变的情况下,我们已经证实它比传统算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On a novel adaptive self organizing network
In this paper a new algorithm is presented in order to overcome the stability vs. formation ability dilemma of competitive learning. This algorithm is based on growing cell structures of self-organizing mapping. The new algorithm is effective for endless learning and automatic classification. Applying the algorithm in the case where the input pattern is changed temporally, we have confirmed that it has much better performance than conventional algorithms.
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