English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 56738/90513 (63%)
造访人次 : 12091053      在线人数 : 55
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46353

    题名: Adaptive Hyper-Fuzzy Partition Particle Swarm Optimization Clustering Algorithm
    作者: Feng, Hsuan-ming;Chen, Ching-yi;余繁;Ye, Fun
    贡献者: 淡江大學電機工程學系
    日期: 2006-08-01
    上传时间: 2010-08-10 10:59:26 (UTC+8)
    出版者: Taylor & Francis
    摘要: This article presents an adaptive hyper-fuzzy partition particle swarm optimization clustering algorithm to optimally classify different geometrical structure data sets into correct groups. In this architecture, we use a novel hyper-fuzzy partition metric to improve the traditional common-used Euclidean norm metric clustering method. Since one fuzzy rule describes one pattern feature and implies the detection of one cluster center, it is encouraged to decrease the number of fuzzy rules with the hyper-fuzzy partition metric. According to the adaptive particle swarm optimization, it is very suitable to manage the clustering task for a complex, irregular, and high dimensional data set. To demonstrate the robustness of the proposed adaptive hyper-fuzzy partition particle swarm optimization clustering algorithms, various clustering simulations are experimentally compared with K -means and fuzzy c-means learning methods.
    關聯: Cybernetics and Systems 37(5), pp.463-479
    DOI: 10.1080/01969720600683429
    显示于类别:[電機工程學系暨研究所] 期刊論文


    档案 大小格式浏览次数
    0196-9722_37(5)p463-479.pdf1371KbAdobe PDF298检视/开启



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈