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

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 Abstract: 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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Language(s): eng - English
 Dates: 2022-01-062022-09
 Publication Status: Finally published
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: 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
 Degree: -

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Title: IEEE Transactions on Control of Network Systems
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 9 (3) Sequence Number: - Start / End Page: 1251 - 1260 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/2325-5870
Publisher: Institute of Electrical and Electronics Engineers (IEEE)