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CLC number: TP13
On-line Access: 2020-03-04
Received: 2019-08-29
Revision Accepted: 2019-10-21
Crosschecked: 2019-12-11
Cited: 0
Clicked: 4964
Citations: Bibtex RefMan EndNote GB/T7714
Bisimulation-based stabilization of probabilistic Boolean control networks with state feedback control
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Nan Jiang, Chi Huang, Yao Chen, Jürgen Kurths. Bisimulation-based stabilization of probabilistic Boolean control networks with state feedback control[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 268-280.
@article{title="Bisimulation-based stabilization of probabilistic Boolean control networks with state feedback control",
author="Nan Jiang, Chi Huang, Yao Chen, Jürgen Kurths",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="2",
pages="268-280",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900447"
}
%0 Journal Article
%T Bisimulation-based stabilization of probabilistic Boolean control networks with state feedback control
%A Nan Jiang
%A Chi Huang
%A Yao Chen
%A Jürgen Kurths
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 2
%P 268-280
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900447
TY - JOUR
T1 - Bisimulation-based stabilization of probabilistic Boolean control networks with state feedback control
A1 - Nan Jiang
A1 - Chi Huang
A1 - Yao Chen
A1 - Jürgen Kurths
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 2
SP - 268
EP - 280
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900447
Abstract: This study is concerned with probabilistic Boolean control networks (PBCNs) with state feedback control. A novel definition of bisimilar PBCNs is proposed to lower computational complexity. To understand more on bisimulation relations between PBCNs, we resort to a powerful matrix manipulation called semi-tensor product (STP). Because stabilization of networks is of critical importance, the propagation of stabilization with probability one between bisimilar PBCNs is then considered and proved to be attainable. Additionally, the transient periods (the maximum number of steps to implement stabilization) of two PBCNs are certified to be identical if these two networks are paired with a bisimulation relation. The results are then extended to the probabilistic Boolean networks.
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