Structure-based clustering algorithm for model reduction of large-scale network systems

Muhammad Umar B. Niazi, Xiaodong Cheng, Carlos Canudas-De-Wit, Jacquelien M.A. Scherpen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.

Original languageEnglish
Title of host publication2019 IEEE 58th Conference on Decision and Control, CDC 2019
PublisherInstitute of Electrical and Electronics Engineers
Pages5038-5043
Number of pages6
ISBN (Electronic)9781728113982
DOIs
Publication statusPublished - 12 Mar 2020
Event58th IEEE Conference on Decision and Control (CDC 2019) - Nice, France
Duration: 11 Dec 201913 Dec 2019
https://cdc2019.ieeecss.org/

Conference

Conference58th IEEE Conference on Decision and Control (CDC 2019)
Abbreviated titleCDC 2019
CountryFrance
CityNice
Period11/12/1913/12/19
Internet address

Keywords

  • clustering algorithm
  • Large-scale systems
  • lumpa-bility
  • model reduction

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