...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neurocomputing >Cluster analysis based on attractor particle swarm optimization with boundary zoomed for working conditions classification of power plant pulverizing system
【24h】

Cluster analysis based on attractor particle swarm optimization with boundary zoomed for working conditions classification of power plant pulverizing system

机译：基于边界吸引子粒子群优化的聚类分析在电厂制粉系统工作条件分​​类中的应用。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper proposes a cluster analysis method based on Attractor Particle Swarm Optimization with Boundary Zoomed (APSO-BZ) for working conditions classification of power plant pulverizing system. The proposed method could be used on the field data directly and the obtained clusters represent the different working conditions of the power plant pulverizing system. For APSO-BZ, the particle position is updated based on the attractor which equals the random modified value of the own optimal or the global optimal. The boundary zoomed strategy is presented for letting a particle flying outside of the search space be relocated based on the positions of the particle and the attractor. Moreover, the sum of the symmetrical compactness of each cluster is adopted as the fitness function for APSO-BZ. Three real-life datasets from UCI Machine Learning Repository and a field dataset of a real power plant pulverizing system are adopted to evaluate the effectiveness of the proposed method. The experiments results verify that the proposed method has higher clustering capability and avoids the premature convergence under a certain extent. Moreover, the proposed method would implement the working conditions classification more correctly.
机译：针对电厂制粉系统的工况分类问题，提出了一种基于边界吸引子粒子群优化（APSO-BZ）的聚类分析方法。所提出的方法可以直接用于现场数据，所获得的集群代表了电厂制粉系统的不同工作条件。对于APSO-BZ，基于吸引子更新粒子位置，该吸引子等于自身最优或全局最优的随机修改值。提出了边界缩放策略，该策略用于根据粒子和吸引子的位置来重新放置从搜索空间飞出的粒子。此外，每个簇的对称紧度之和被用作APSO-BZ的适应度函数。本文采用UCI机器学习存储库中的三个真实数据集和一个真实电厂制粉系统的现场数据集来评估该方法的有效性。实验结果证明，该方法具有较高的聚类能力，在一定程度上避免了过早的收敛。此外，所提出的方法将更正确地实现工作条件分​​类。

著录项

  • 来源
    《Neurocomputing》 |2013年第6期|54-63|共10页
  • 作者

    Hui Cao; Wenquan Chen; Lixin Jia; Yanbin Zhang; Yantao Lu;

  • 作者单位

    State Key Laboratory of Electrical Insulation and Power Equipment, Electrical Engineering School, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

    State Key Laboratory of Electrical Insulation and Power Equipment, Electrical Engineering School, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

    State Key Laboratory of Electrical Insulation and Power Equipment, Electrical Engineering School, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

    State Key Laboratory of Electrical Insulation and Power Equipment, Electrical Engineering School, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

    State Key Laboratory of Electrical Insulation and Power Equipment, Electrical Engineering School, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Pulverizing system; Working conditions classification; Cluster analysis; Attractor particle swarm optimization;

    机译：制粉系统工作条件分​​类;聚类分析;吸引子粒子群优化;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号