Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220200068-6.doi: 10.11896/jsjkx.220200068

• Artificial Intelligence • Previous Articles     Next Articles

Observability of Probabilistic Boolean Control Networks

FAN Zhuoyou   

  1. School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo,Henan 454000,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:FAN Zhuoyou,born in 1997,postgra-duate.Her main research interests include Boolean control networks and information processing.

Abstract: The uncertainty of the transfer matrix brings difficulties to the observability and controllability analysis for probabilistic Boolean control networks(PBCNs).This paper mainly studies the observability of PBCNs,and the conditions of observability are also developed for PBCNs.On this basis,the method for calculating the initial state vector of the system is given.Firstly,according to the reachable state set of PBCNs,the distinguishable and indistinguishable states of the system are defined,and the concept of $d$-step distinguishability and the necessary and sufficient conditions for its judgment are given.Secondly,based on the output and state model of PBCNs,the probabilistic initial state set of the system is also obtained.Then,the definition of strong observability and weak observability of PBCNs are given.Meanwhile,the methods of calculating the initial state vector and determining whether a given PBCN is observable are obtained.Finally,an example is given to illustrate the effectiveness of the proposed methods.

Key words: Probabilistic Boolean control networks, Reachable state set, Observability

CLC Number: 

  • TP181
[1]KAUFFMAN S A.Metabolic stability and epigenesis in randomly constructed genetic nets[J].Journal of Theoretical Biology,1969,22(3):437-467.
[2]CHENG D,QI H,ZHAO Y.An Introduction to Semi-Tensor Product of Matrices and Its Applications[M].World Scientific,2012.
[3]CHENG D,QI H,LI Z.Analysis and Control of Boolean Networks[M].London:Springer London,2011.
[4]CHENG D,QI H.A Linear Representation of Dynamics ofBoolean Networks[J].IEEE Transactions on Automatic Control,2010,55(10):2251-2258.
[5]GUO Y,WANG P,GUI W,et al.Set stability and set stabilization of Boolean control networks based on invariant subsets[J].Automatica,2015,61:106-112.
[6]LI R,YANG M,CHU T.State Feedback Stabilization for Boolean Control Networks[J].IEEE Transactions on Automatic Control,2013,58(7):1853-1857.
[7]LI H,WANG Y.Output feedback stabilization control designfor Boolean control networks[J].Automatica,2013,49(12):3641-3645.
[8]ZHANG K,ZHANG L.Observability of Boolean control net-works:A unified approach based on the theories of finite automa-ta[J].IEEE Transactions on Automatic Control,2016,61(9):2733-2738.
[9]LASCHOV D,MARGALIOT M,EVEN G.Observability ofBoolean networks:A graph-theoretic approach[J].Automatica,2013,49(8):2351-2362.
[10]LASCHOV D,MARGALIOT M.Controllability of Booleancontrol networks via the Perron-Frobenius theory[J].Automatica,2012,48(6):1218-1223.
[11]CHENG D,ZHAO Y.Identification of Boolean control networks[J].Automatica,2011,47(4):702-710.
[12]LI F,LU X,YU Z.Optimal control algorithms for switchedBoolean network[J].Journal of the Franklin Institute,2014,351(6):3490-3501.
[13]FORNASINI E,VALCHER M E.Optimal control of Booleancontrol networks[J].IEEE Transactions on Automatic Control,2014,59:1258-1270.
[14]LIU Y,LI B,LOU J.Disturbance Decoupling of Singular BooleanControl Networks[J].IEEE/ACM Transactions on Computational Biology and Bioinformatics,2016,13(6):1194-1200.
[15]CHENG D.Disturbance Decoupling of Boolean Control Net-works[J].IEEE Transactions on Automatic Control,2011,56(1):2-10.
[16]SHMULEVICH I,DOUGHERTY E R,ZHANG W.FromBoolean to probabilistic Boolean networks as models of genetic regulatory networks[C]//Proceedings of the IEEE.2002.
[17]SHMULEVICH I,DOUGHERTY E R,KIM S,et al.Probabilistic Boolean networks:a rule-based uncertainty model for gene regulatory networks[J].Bioinformatics,2002,18(2):261-274.
[18]LI Z,XIAO H.Weak reachability of probabilistic boolean control networks[C]//2015 International Conference on Advanced Mechatronic Systems(ICAMechS).2015:56-60.
[19]ZHOU R,GUO Y,GUI W.Set reachability and observability of probabilistic Boolean networks[J].Automatica,2019,106:230-241.
[20]ZHANG Q,FENG J,WANG B.Stability analysis of probabilistic Boolean networks with switching topology[J].Nonlinear Analysis:Hybrid Systems,2021,42:101076.
[21]GUO Y,ZHOU R,WU Y,et al.Stability and Set Stability inDistribution of Probabilistic Boolean Networks[J].IEEE Tran-sactions on Automatic Control,2018:1-1.
[22]ZHAO Y,CHENG D.On controllability and stabilizability ofprobabilistic Boolean control networks[J].Science China Information Sciences,2014,57(1):1-14.
[23]LI R,YANG M,CHU T.State feedback stabilization for probabilistic Boolean networks[J].Automatica,2014,50(4):1272-1278.
[24]LIU Q.Optimal finite horizon control in gene regulatory net-works[J].The European Physical Journal B,2013,86(6):245.
[25]PAL R,DATTA A,DOUGHERTY E R.Optimal infinite-horizon control for probabilistic Boolean networks[J].IEEE Transactions on Signal Processing,2006,54(6):2375-23870.
[26]FORNASINI E,VALCHER M E.Observability and Recon-structibility of Probabilistic Boolean Networks[J].IEEE Control Systems Letters,2020,4(2):319-324.
[27]LI F,SUN J.Controllability of probabilistic Boolean control networks[J].Automatica,2011,47(12):2765-2771.
[28]LIU Y,CHEN H,LU J,et al.Controllability of probabilisticBoolean control networks based on transition probability matrices[J].Automatica,2015,52:340-345.
[29]GOU Z L,XU Y,WANG J H.Set Controllability of Probabilistic Boolean Control Networks[J].Control Theory and Applications,2021(5):689-696.
[30]ZHAO Y,CHENG D.On controllability and stabilizability of probabilistic Boolean control networks[J].Science China Information Sciences,2014,57(1):1-14.
[31]WANG L,LIU Y,WU Z G,et al.Stabilization and Finite-Time Stabilization of Probabilistic Boolean Control Networks[J/OL].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2019:1-8.DOI:10.1109/TSMC.2019.2898880.
[32]WANG B,FENG J E.Recent Development on Observability and Detectability of Boolean Control Networks[J].Control and Decision,2020,35(9):2049-2058.
[33]JING Z,ZHENBIN L.Observability of probabilistic Booleannetworks[C]//2015 34th Chinese Control Conference(CCC).Hangzhou,China:IEEE,2015:183-186.
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