Stability and Pinning Stabilization of Markovian Jump Boolean Networks
Min Meng, Li Li
Abstract
This brief investigates stability and pinning stabilization of Markovian jump Boolean networks (MJBNs) based on semi-tensor product of matrices. New stability conditions for MJBNs are obtained directly by the algebraic expression of MJBNs, which lays a foundation for pinning control of MJBNs. Then, two algorithms are devised, one of which can get a stochastically stable MJBN easily and the other one generates a set of pinning nodes with the smallest cardinality. Moreover, all the feasible pinning controllers can be designed by solving a group of matrix equations. Illustrative examples are presented to show the effectiveness of the obtained results.
Topics & Concepts
JumpStability (learning theory)Cardinality (data modeling)MathematicsSet (abstract data type)Matrix (chemical analysis)Markov processAlgebraic numberApplied mathematicsControl theory (sociology)Computer scienceControl (management)Mathematical analysisPhysicsStatisticsProgramming languageMachine learningComposite materialData miningArtificial intelligenceQuantum mechanicsMaterials scienceGene Regulatory Network AnalysisAdvanced Fluorescence Microscopy TechniquesReceptor Mechanisms and Signaling