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Related Experiment Video

Updated: Jun 29, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Set stabilization of logical control networks: A minimum node control approach.

Jiayang Liu1, Lina Wang2, Amol Yerudkar3

  • 1School of International Business, Jinhua Open University, Jinhua, 321022, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces efficient algorithms for set stabilization in probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs) using minimal node control. The research optimizes control strategies for network stabilization, reducing costs and improving efficiency.

Keywords:
Logical networksPinning controllerProbabilistic Boolean control networksSet stabilizationState-feedback stabilization

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Area of Science:

  • Network systems
  • Control theory
  • Computational biology

Background:

  • Pinning control and minimum node control are cost-effective strategies for network stabilization.
  • Set stabilization is a crucial problem in logical control networks, including probabilistic Boolean networks (PBNs) and probabilistic Boolean control networks (PBCNs).

Purpose of the Study:

  • To investigate the set stabilization problem in probabilistic Boolean networks and probabilistic Boolean control networks using minimal node control.
  • To develop and optimize algorithms for identifying minimum pinning node sets and designing controllers.

Main Methods:

  • An initial algorithm was developed to find the minimum index set of pinning nodes.
  • Optimized algorithms with reduced computational complexity were presented for network control using minimum node sets.
  • Sufficient and necessary conditions were established to guarantee the feasibility and effectiveness of the proposed algorithms.
  • A theorem was formulated for probabilistic Boolean control networks to determine all state-feedback controllers for a given set of pinning nodes.

Main Results:

  • The study successfully identified minimum pinning node sets for set stabilization in PBNs and PBCNs.
  • Optimized algorithms demonstrated lower computational complexity compared to the initial approach.
  • The proposed methods provide conditions for ensuring control feasibility and effectiveness.
  • A method for devising state-feedback controllers for PBCNs was established.

Conclusions:

  • The research provides efficient computational methods for set stabilization in probabilistic Boolean networks and control networks.
  • The findings are validated through application to gene regulatory network models, showcasing practical efficacy.
  • The developed techniques contribute to cost-effective network control and stabilization strategies.