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

doi:10.1109/CDC40024.2019.9029349

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	2019 IEEE 58th Conference on Decision and Control, CDC 2019
Publisher	Institute of Electrical and Electronics Engineers
Pages	5038-5043
Number of pages	6
ISBN (Electronic)	9781728113982
DOIs	https://doi.org/10.1109/CDC40024.2019.9029349
Publication status	Published - 12 Mar 2020
Event	58th IEEE Conference on Decision and Control (CDC 2019) - Nice, France Duration: 11 Dec 2019 → 13 Dec 2019 https://cdc2019.ieeecss.org/

Conference

Conference	58th IEEE Conference on Decision and Control (CDC 2019)
Abbreviated title	CDC 2019
Country	France
City	Nice
Period	11/12/19 → 13/12/19
Internet address	https://cdc2019.ieeecss.org/

Keywords

clustering algorithm
Large-scale systems
lumpa-bility
model reduction

Access to Document

10.1109/CDC40024.2019.9029349

Link to publication in Scopus

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Cite this

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title = "Structure-based clustering algorithm for model reduction of large-scale network systems",

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.",

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Niazi, MUB, Cheng, X, Canudas-De-Wit, C & Scherpen, JMA 2020, Structure-based clustering algorithm for model reduction of large-scale network systems. in 2019 IEEE 58th Conference on Decision and Control, CDC 2019., 9029349, Institute of Electrical and Electronics Engineers, pp. 5038-5043, 58th IEEE Conference on Decision and Control (CDC 2019), Nice, France, 11/12/19. https://doi.org/10.1109/CDC40024.2019.9029349

Structure-based clustering algorithm for model reduction of large-scale network systems. / Niazi, Muhammad Umar B.; Cheng, Xiaodong; Canudas-De-Wit, Carlos; Scherpen, Jacquelien M.A.

2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers, 2020. p. 5038-5043 9029349.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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