Abstract
Networked dynamic systems are ubiquitous in various domains, such as industrial processes, social networks, and biological systems. These systems produce high-dimensional data that reflect the complex interactions among the network nodes with rich sensor measurements. In this paper, we propose a novel algorithm for latent dynamic networked system identification that leverages the network structure and performs dimension reduction for each node via dynamic latent variables (DLVs). The algorithm assumes that the DLVs of each node have an auto-regressive model with exogenous input and interactions from other nodes. The DLVs of each node are extracted to capture the most predictable latent variables in the high dimensional data, while the residual factors are not predictable. The advantage of the proposed framework is demonstrated on an industrial process network for system identification and dynamic data analytics.
Original language | English |
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Title of host publication | Proceedings : 2023 62nd IEEE Conference on Decision and Control, CDC 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 461-466 |
Number of pages | 6 |
ISBN (Electronic) | 9798350301243 |
DOIs | |
Publication status | Published - 2023 |
Event | 62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: 13 Dec 2023 → 15 Dec 2023 |
Publication series
Name | Proceedings of the IEEE Conference on Decision and Control |
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Publisher | IEEE |
ISSN (Print) | 0743-1546 |
ISSN (Electronic) | 2576-2370 |
Conference
Conference | 62nd IEEE Conference on Decision and Control, CDC 2023 |
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Country/Territory | Singapore |
City | Singapore |
Period | 13/12/23 → 15/12/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.