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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.
https://doi.org/10.1109/TCNS.2022.3141023

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 Urheber:
Chen, Bingquan1, Autor
Cao, Jinde 1, Autor
Gorbachev, Sergey1, Autor
Liu, Yang1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: 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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Sprache(n): eng - Englisch
 Datum: 2022-01-062022-09
 Publikationsstatus: Final veröffentlicht
 Seiten: 10
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

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Titel: IEEE Transactions on Control of Network Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 9 (3) Artikelnummer: - Start- / Endseite: 1251 - 1260 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/2325-5870
Publisher: Institute of Electrical and Electronics Engineers (IEEE)