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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel2019 IEEE 58th Conference on Decision and Control, CDC 2019
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's5038-5043
Aantal pagina's6
ISBN van elektronische versie9781728113982
DOI's
StatusGepubliceerd - 12 mrt 2020
Evenement58th IEEE Conference on Decision and Control, CDC 2019 - Nice, Frankrijk
Duur: 11 dec 201913 dec 2019

Congres

Congres58th IEEE Conference on Decision and Control, CDC 2019
LandFrankrijk
StadNice
Periode11/12/1913/12/19

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  • Citeer dit

    Niazi, M. U. B., Cheng, X., Canudas-De-Wit, C., & Scherpen, J. M. A. (2020). Structure-based clustering algorithm for model reduction of large-scale network systems. In 2019 IEEE 58th Conference on Decision and Control, CDC 2019 (blz. 5038-5043). [9029349] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CDC40024.2019.9029349