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Set reachability of Markovian jump Boolean networks and its applications. (English) Zbl 07916990

Summary: This study investigates the set reachability of Markovian jump Boolean networks (MJBNs). Firstly, through comprehensive analysis, two kinds of set reachability, strong set reachability and weak set reachability, are defined. By using semi-tensor product of matrices, the considered MJBN is converted into a Markov chain which can not only describe the state transition of the MJBN synchronously, but also maintain its transition probability. Based on this, the set reachability problem of the MJBN is transformed into the reachability and convergence problems of the corresponding Markov chain. Then some necessary and sufficient conditions for set reachability of MJBNs are obtained. Finally, the observability and output stability problems of MJBNs are discussed as two applications of set reachability.
© 2021 The Authors. IET Control Theory & Applications published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology

MSC:

93B03 Attainable sets, reachability
93E03 Stochastic systems in control theory (general)
93C29 Boolean control/observation systems
93B70 Networked control
Full Text: DOI

References:

[1] KauffmanS.A.: ‘Metabolic stability and epigenesis in randomly constructed genetic nets’, J. Theor. Biol., 1969, 22, (3), pp. 437-467
[2] HeidelJ.MaloneyJ.FarrowC. et al.: ‘Finding cycles in synchronous Boolean networks with applications to biochemical systems’, Int. J. Bifurcation Chaos, 2003, 13, (3), pp. 535-552 · Zbl 1056.37013
[3] AkutsuT.MiyanoS.KuharaS.: ‘Inferring qualitative relations in genetic networks and metabolic pathway’, Bioinformatics, 2000, 16, (8), pp. 727-734
[4] AkutsuT.HayashidaM.ChingW.K. et al.: ‘Control of Boolean networks: hardness results and algorithms for tree structured networks’, J. Theor. Biol., 2007, 244, (4), pp. 670-679 · Zbl 1450.92040
[5] AlbertR.BarabasA.L.: ‘Dynamics of complex systems: scaling laws or the period of Boolean networks’, Phys. Rev. Lett., 2000, 84, (24), pp. 5660-5663
[6] ZouY.L.ZhuJ.D.LiuY.: ‘State‐feedback controller design for disturbance decoupling of Boolean control networks’, IET Control Theory Appl., 2017, 11, (18), pp. 3233-3239
[7] MaY.L.HanR.Z.: ‘Algorithms for set stabilisation of Boolean control networks’, IET Control Theory Appl., 2018, 12, (10), pp. 1527-1532
[8] LiangJ.L.ChenH.W.LiuY.: ‘On algorithms for state feedback stabilization of Boolean control networks’, Automatica, 2017, 84, pp. 10-16 · Zbl 1376.93083
[9] WuY.H.SunX.M.ZhaoX.D. et al.: ‘Optimal control of Boolean control networks with average cost: a policy iteration approach’, Automatica, 2019, 100, pp. 378-387 · Zbl 1415.93043
[10] YangM.ChuT.G.: ‘Evaluation of attractors and basins of asynchronous random Boolean networks’, Phy. Rev. E, 2017, 85, (5), pp. 056105‐1-056105‐6
[11] LiH.T.WangY.Z.LiuZ.B.: ‘Stability analysis for switched Boolean networks under arbitrary switching signals’, IEEE Trans. Autom. Control, 2014, 59, (7), pp. 1978-1982 · Zbl 1360.93502
[12] YuY.Y.MengM.FengJ.E. et al.: ‘An adjoint network approach to design stabilizable switching signals of switched Boolean networks’, Appl. Math. Comput., 2019, 357, pp. 12-22 · Zbl 1428.93057
[13] ChenH.W.LiangJ.L.HuangT.W. et al.: ‘Synchronization of arbitrarily switched Boolean networks’, IEEE Trans. Neural Netw. Learning, 2017, 28, (3), pp. 612-619
[14] LiuY.ChenH.W.LuJ.Q. et al.: ‘Controllability of probabilistic Boolean control networks based on transition probability matrices’, Automatica, 2015, 52, pp. 340-345 · Zbl 1309.93026
[15] ZhouR.P.GuoY.Q.GuiW.H.: ‘Set reachability and observability of probabilistic Boolean networks’, Automatica, 2019, 106, pp. 230-241 · Zbl 1429.93034
[16] GuoY.Q.ZhouR.P.WuY.H. et al.: ‘Stability and set stability in distribution of probabilistic Boolean networks’, IEEE Trans. Autom. Control, 2019, 64, (2), pp. 736-742 · Zbl 1482.60037
[17] LiuY.WangL.Q.LuJ.Q. et al.: ‘Sampled‐data stabilization of probabilistic Boolean control networks’, Syst. Control Lett., 2019, 124, pp. 106-111 · Zbl 1408.93099
[18] LiR.YangM.ChuT.G.: ‘State feedback stabilization for probabilistic Boolean networks’, Automatica, 2014, 50, (4), pp. 1272-1278 · Zbl 1298.93275
[19] WangB.FengJ.E.: ‘On detectability of probabilistic Boolean networks’, Inf. Sci., 2019, 483, pp. 383-395
[20] LiH.T.WangY.Z.GuoP.L.: ‘State feedback based output tracking control of probabilistic Boolean networks’, Inf. Sci., 2016, 349-350, pp. 1-11 · Zbl 1398.93124
[21] LiangJ.L.LamJ.WangZ.D.: ‘state estimation for markov‐type genetic regulatory networks with delays and uncertain mode transition rates’, Phys. Lett. A, 2009, 373, (47), pp. 4328-4337 · Zbl 1234.92023
[22] LiQ.ShenB.LiuY.: ‘Event‐triggered \(H^{\operatorname{\infty}}\) state estimation for discrete‐time stochastic genetic regulatory networks with Markovian jumping parameters and time‐varying delays’, Neurocomputing, 2016, 174, pp. 912-920
[23] LiuJ.L.YueD.: ‘Asymptotic stability of Markovian jumping genetic regulatory networks with random delays’, Chin. J. Electron., 2013, 22, (2), pp. 263-268
[24] MengM.LiuL.FengG.: ‘Stability and \(l_1\) gain analysis of Boolean networks with Markovian jump parameters’, IEEE Trans. Autom. Control, 2017, 62, (8), pp. 4222-4228 · Zbl 1373.93368
[25] MengM.XiaoG.X.ZhaiC. et al.: ‘Controllability of Markovian jump Boolean control networks’, Automatica, 2019, 106, pp. 70-76 · Zbl 1429.93040
[26] PossieriC.TeelA.R.: ‘Weak reachability and strong recurrence for stochastic directed graphs in terms of auxiliary functions’. Proc. Int. Conf. IEEE 55th Conf. on Decision and Control, Las Vegas, USA, December 2016, pp. 3714-3719
[27] PossieriC.TeelA.R.: ‘Asymptotic stability in probability for stochastic Boolean networks’, Automatica, 2017, 83, pp. 1-9 · Zbl 1373.93369
[28] LiuH.C.LiuY.LiY.Y. et al.: ‘Observability of Boolean networks via STP and graph methods’, IET Control Theory Appl., 2019, 13, (7), pp. 1031-1037 · Zbl 1432.93039
[29] ZhangK.Z.ZhangL.J.XieL.H.: ‘Finite automata approach to observability of switched Boolean control networks’, Nonlinear Anal., Hybrid Syst., 2016, 19, pp. 186-197 · Zbl 1343.93021
[30] LiZ.Q.XiaoH.M.: ‘Output stability and stabilization of Boolean control networks’, Control Theory Appl., 2017, 34, (9), pp. 1201-1207 · Zbl 1399.93199
[31] ChengD.Z.QiH.S.LiZ.Q.: ‘Analysis and control of Boolean networks: a semi‐tensor product approach’ (Springer, London, 2011) · Zbl 1209.93001
[32] MengM.LamJ.FengJ.E. et al.: ‘\(l_1\) ‐gain analysis and model reduction problem for Boolean control networks’, Inf. Sci., 2016, 348, pp. 68-83 · Zbl 1398.93151
[33] NaldiA.RemyR.ThieffryD. et al.: ‘Dynamically consistent reduction of logical regulatory graphs’, Theor. Comput. Sci., 2011, 412, (21), pp. 2207-2218 · Zbl 1211.92024
[34] Veliz‐CubaA.: ‘Reduction of Boolean network models’, J. Theor. Biol., 2011, 289, pp. 167-172 · Zbl 1397.92265
[35] ZhongJ.LiB.W.LiuY. et al.: ‘Steady‐state design of large‐dimensional Boolean networks’, IEEE Trans. Neural Netw. Learning, 2020, doi: 10.1109/TNNLS.2020.2980632, pp. 1-13
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