Tagged potential field extension to self-organizing feature maps

N. Baykal, A. Erkmen
{"title":"Tagged potential field extension to self-organizing feature maps","authors":"N. Baykal, A. Erkmen","doi":"10.1109/KES.1998.725925","DOIUrl":null,"url":null,"abstract":"Proposes an escape methodology to the local minima problem of self-organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of the self-organizing feature map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm while increasing the probability of escaping from local minima. In the second approach we associate a learning set which specifies attractive and repulsive fields of output neurons. Results indicate that accuracy percentile of the new methods are higher than the original algorithm while they have the ability to escape from local minima.","PeriodicalId":394492,"journal":{"name":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"1998 Second International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No.98EX111)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/KES.1998.725925","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

Proposes an escape methodology to the local minima problem of self-organizing feature maps generated in the overlapping regions which are equidistant to the corresponding winners. Two new versions of the self-organizing feature map are derived equipped with such a methodology. The first approach introduces an excitation term, which increases the convergence speed and efficiency of the algorithm while increasing the probability of escaping from local minima. In the second approach we associate a learning set which specifies attractive and repulsive fields of output neurons. Results indicate that accuracy percentile of the new methods are higher than the original algorithm while they have the ability to escape from local minima.
标记势场扩展到自组织特征映射
针对自组织特征映射的局部极小问题,提出了一种逃避方法。采用这种方法导出了两个新的自组织特征映射版本。第一种方法引入激励项,提高了算法的收敛速度和效率,同时增加了逃离局部极小值的概率。在第二种方法中,我们将一个指定输出神经元的吸引和排斥场的学习集关联起来。结果表明,新方法的准确率比原算法有所提高,同时具有摆脱局部极小值的能力。
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