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  Pinning Asymptotic Stabilization of Probabilistic Boolean Networks: A Digraph Approach

Chen, B., Cao, J., Gorbachev, S., Liu, Y., & Kurths, J. (2022). Pinning Asymptotic Stabilization of Probabilistic Boolean Networks: A Digraph Approach. IEEE Transactions on Control of Network Systems, 9(3), 1251-1260. doi:10.1109/TCNS.2022.3141023.

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資料種別: 学術論文

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 作成者:
Chen, Bingquan1, 著者
Cao, Jinde 1, 著者
Gorbachev, Sergey1, 著者
Liu, Yang1, 著者
Kurths, Jürgen2, 著者              
所属:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 要旨: This article investigates the pinning asymptotic stabilization of probabilistic Boolean networks (PBNs) by a digraph approach. In order to stabilize the PBN asymptotically, a mode-independent pinning control (MIPC) is designed to make the target state a fixed point, and transform the state transition digraph into one that has a spanning branching rooted at the target vertex. It is shown that if there is a mode-dependent pinning control that can asymptotically stabilize the PBN, then there must exist an MIPC that can do the same with fewer pinned nodes and control inputs. A necessary and sufficient condition is given to verify the feasibility of a set of pinned nodes based on an auxiliary digraph. Three algorithms are proposed to find a feasible set of pinned nodes with the minimum cardinality. The main results are extended to the case where the target is a limit cycle. Compared with the existing results, the total computational complexity of these algorithms is strongly reduced. The obtained results are applied to a numerical example and a cell apoptosis network.

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言語: eng - 英語
 日付: 2022-01-062022-09
 出版の状態: Finally published
 ページ: 10
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1109/TCNS.2022.3141023
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Working Group: Network- and machine-learning-based prediction of extreme events
 学位: -

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出版物 1

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出版物名: IEEE Transactions on Control of Network Systems
種別: 学術雑誌, SCI, Scopus
 著者・編者:
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出版社, 出版地: -
ページ: - 巻号: 9 (3) 通巻号: - 開始・終了ページ: 1251 - 1260 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/2325-5870
Publisher: Institute of Electrical and Electronics Engineers (IEEE)