Stability and Stabilization in Probability of Probabilistic Boolean Networks

Chi Huang, Jianquan Lu, Guisheng Zhai, Jinde Cao, Guoping Lu, Matjaz Perc

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

This article studies the stability in probability of probabilistic Boolean networks and stabilization in the probability of probabilistic Boolean control networks. To simulate more realistic cellular systems, the probability of stability/stabilization is not required to be a strict one. In this situation, the target state is indefinite to have a probability of transferring to itself. Thus, it is a challenging extension of the traditional probability-one problem, in which the self-transfer probability of the target state must be one. Some necessary and sufficient conditions are proposed via the semitensor product of matrices. Illustrative examples are also given to show the effectiveness of the derived results.

Original languageEnglish
Article number9046240
Pages (from-to)241-251
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number1
DOIs
Publication statusPublished - 2021 Jan

Keywords

  • Probabilistic Boolean network (PBN)
  • semitensor product (STP)
  • stability/stabilization in probability
  • state feedback control

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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