A Nonaugmented Method for the Minimal Observability of Boolean Networks

  • Yifeng LI
  • , Baoyu LIU
  • , Xuewen LIU
  • , Zhichun YANG
  • , Yongduan SONG*
  • *Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

1 Citation (Scopus)

Abstract

This article proposes a nonaugmented method for investigating the minimal observability problem of Boolean networks (BNs). This method can be applied to more general BNs and reduce the computational and space complexity of existing results. First, unobservable states concerning an unobservable BN are classified into three categories using the vertex-colored state transition graph, each accompanied by a necessary and sufficient condition for determining additional measurements to make them distinguishable. Then, an algorithm is designed to identify the additional measurements that would render an unobservable BN observable using the conditions. Next, to determine the minimum added measurements, a necessary and sufficient condition and an algorithm based on a constructed matrix are presented. Finally, the results obtained are compared with existing literature and illustrated with examples.
Original languageEnglish
Pages (from-to)7981-7990
Number of pages10
JournalIEEE Transactions on Cybernetics
Volume54
Issue number12
DOIs
Publication statusPublished - Dec 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62403088 and Grant 12471153; in part by the Natural Science Foundation of Chongqing under Grant CSTB2022NSCQ-MSX0325; in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant KJQN202200524 and Grant KJZDK202400503; and in part by the Innovation and Development Joint Fund of Chongqing Natural Science Foundation under Grant CSTB2023NSCQ-LZX0142.

Keywords

  • Boolean networks (BNs)
  • minimal observability
  • nonaugmented method
  • semi-tensor product (STP)

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