加权有向图社区发现的子系统划分
Weighted directed graph based community detection for subsystem decomposition
摘要点击 70  全文点击 40  投稿时间:2019-08-01  修订日期:2020-03-21
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DOI编号  10.7641/CTA.2020.90632
  2020,37(9):1923-1930
中文关键词  子系统划分  滚动时域估计  社区发现算法  加权有向图
英文关键词  subsystem decomposition  moving horizon estimation  community detection  weighted directed graph
基金项目  国家自然科学基金项目(61803161, 61973125), 江门市创新科研团队引进基金项目(2017TD03)资助.
作者单位E-mail
杨晓峰 华南理工大学 xfyangscut@163.com 
谢巍 华南理工大学  
张浪文 华南理工大学 aulwzhang@scut.edu.cn 
中文摘要
      提出一种基于加权有向图的社区发现子系统划分方法, 并应用于分布式状态估计设计. 针对一类复杂非线 性系统, 构建考虑连接边强度的加权有向图, 引入社区发现算法将复杂非线性系统划分成多个子系统. 同时考虑子 系统之间连接边的数量和有向图顶点之间的连接强度, 使得划分得到的子系统内部关联较强, 而子系统之间的耦合 强度较弱. 针对划分得到的子系统, 设计基于信息交互的分布式滚动时域估计算法, 并与已有的子系统划分方法对 比, 在相同的状态估计设定下, 所提出的子系统划分方法能够有效提高状态估计的性能.
英文摘要
      This paper presents a subsystem decomposition method based on community structure detection with weighted directed graph. The configured subsystems are used for the design of distributed state estimation. For a complex nonlinear system, a weighted digraph considering the strength of the connected edge is constructed, and a community structure detection is introduced to divide a complex nonlinear system into several subsystems. The proposed subsystem decomposition method takes both the number of connecting edges between subsystems and the connection strength of the vertex of the digraph into account. To this end, the configured subsystems have stronger connections within a subsystem and weaker couplings among the subsystems. For the decomposed subsystem, a distributed moving horizon estimation method based on information interaction is designed. Compared with the existing methods, the proposed method can effectively improve the performance of state estimation for a same distributed state estimation.