A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory
	Jin, Jing 1; Li LJ(李丽娟)1; Zou T(邹涛)2 ; Zhang, Shu 1
	2018
会议日期	MAY 25-27, 2018
会议地点	Enshi, PEOPLES R CHINA
关键词	System decomposition Pearson correlation graph theory
页码	819-824
英文摘要	With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In the traditional system decomposition methods based on graph theory, the weight on each edge of the graph is set by state space equation to reflect the mutual influence of variables in the system. But in the actual industrial process, the acquisition of state space equation is more difficult. In this paper, a system decomposition method based on Pearson correlation coefficient and graph theory is proposed to avoid the use of state space equations. At first, a directed graph is established to represent the actual process of the industrial system and the weights on corresponding edges in the directed graph are set by the Pearson correlation coefficients between two nodes connected by these edges. Then the directed graph is decomposed into several initial subgraphs and the subgraphs will be fused according to a certain rule. Here, a fusion index is defined to select the optimal fusion results in each fusion process. After each fusion process, the termination condition is required to determine whether to continue the next round of fusion process. When the fusion process ends, the subsets obtained at this time are the results of the system decomposition. When the system decomposition is finished, the online subsystems modeling will be carried out by RPLS algorithm. Finally, the proposed algorithm is applied in the Tennessee Eastman process to verify the validity.
源文献作者	Chinese Assoc Automat, Tech Comm Data Driven Control, Learning & Optimizat,, Hubei Univ Nationalities, IEEE Beijing Sect, IEEE Ind Electron Soc, CAA, IEEE, Beijing Jiaotong Univ, IES, ACTA Automatica Sinica, IEEE/CAA Journal of Automatica Sinica
产权排序	2
会议录	PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS)
会议录出版者	IEEE
会议录出版地	NEW YORK
语种	英语
ISBN号	978-1-5386-2618-4
WOS记录号	WOS:000450645900148
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/23656]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Li LJ(李丽娟)
作者单位	1.College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing, 211816, China 2.Industrial Control Networks and Systems Department, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
推荐引用方式 GB/T 7714	Jin, Jing,Li LJ,Zou T,et al. A Novel System Decomposition Method Based on Pearson Correlation and Graph Theory[C]. 见:. Enshi, PEOPLES R CHINA. MAY 25-27, 2018.