Model-Free Dominant Pole Placement for Restabilizing High-Dimensional Network Systems via Small-Sample-Size Data

Xun Shen*, Hampei Sasahara, Masahide Morishita, Jun Ichi Imura, Makito Oku, Kazuyuki Aihara

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

3 被引用数 (Scopus)

抄録

There is a critical transition before a high-dimensional network system completely deteriorates. The Dynamical Network Marker (DNM) theory has been developed for the early prediction of such critical transitions by only using High-Dimension Small-Sample-Size (HDSSS) data. This article presents a model-free dominant pole placement approach for restabilizing the high-dimensional network systems towards avoidance of critical transitions by early treatment. Instead of traditional model-based pole placement, we present a model-free exact dominant pole placement method with dominant eigenvectors of the system matrix, which can be estimated from HDSSS data of system states. We further introduce two approximations of exact dominant pole placement to reduce the complexity of implementing restabilization. The first one is to approximate the right dominant eigenvector-based pole placement by reducing the number of intervened nodes. The second one is to intervene only in the diagonal part of the system matrix. We conduct theoretical analysis and numerical simulations to investigate the performance of the proposed dominant pole placement method.

本文言語英語
ページ(範囲)45572-45585
ページ数14
ジャーナルIEEE Access
11
DOI
出版ステータス出版済み - 2023

ASJC Scopus 主題領域

  • コンピュータサイエンス一般
  • 材料科学一般
  • 工学一般

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