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  Output feedback control for set stabilization of Boolean control networks

Liu, R., Lu, J., Zheng, W. X., Kurths, J. (2020): Output feedback control for set stabilization of Boolean control networks. - IEEE Transactions on Neural Networks and Learning Systems, 31, 6, 2129-2139.
https://doi.org/10.1109/TNNLS.2019.2928028

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_24307 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_24307_2
Genre: Journal Article

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8793216 (Publisher version), 52KB
 
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 Creators:
Liu, Rongjian1, Author
Lu, Jianquan1, Author
Zheng, Wei Xing1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: In this paper, the output feedback set stabilization problem for Boolean control networks (BCNs) is investigated with the help of the semi-tensor product (STP) tool. The concept of output feedback control invariant (OFCI) subset is introduced, and novel methods are developed to obtain the OFCI subsets. Based on the OFCI subsets, a technique, named spanning tree method, is further introduced to calculate all possible output feedback set stabilizers. An example concerning lac operon for the bacterium Escherichia coli is given to illustrate the effectiveness of the proposed method. This technique can also be used to solve the state feedback (set) stabilization problem for BCNs. Compared with the existing results, our method can dramatically reduce the computational cost when designing all possible state feedback stabilizers for BCNs.

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 Dates: 2019-08-092020
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/TNNLS.2019.2928028
PIKDOMAIN: RD4 - Complexity Science
MDB-ID: No data to archive
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: IEEE Transactions on Neural Networks and Learning Systems
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 31 (6) Sequence Number: - Start / End Page: 2129 - 2139 Identifier: Other: Institute of Electrical and Electronics Engineers (IEEE)
Other: 2162-237X
ISSN: 2162-237X
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)