Dimensionality Reduction Method for the Output Regulation of Boolean Control Networks

Shihua Fu, Jun‐e Feng, Yuan Zhao, Jianjun Wang, Jinfeng Pan

2024IEEE Transactions on Neural Networks and Learning Systems16 citationsDOI

Abstract

This article proposes a dimensionality reduction approach to study the output regulation problem (ORP) of Boolean control networks (BCNs), which has much lower computational complexity than previous results. First, an auxiliary system which is much smaller in scale than the augmented system in previous approach is constructed. By analyzing the set stabilization of the auxiliary system as well as the original BCN, a necessary and sufficient condition to detect the solvability of the ORP is presented. Second, a method to design the state feedback controls for the ORP is proposed. Finally, two biological examples are given to demonstrate the effectiveness and advantage of the obtained new results.

Topics & Concepts

Reduction (mathematics)Dimensionality reductionCurse of dimensionalitySet (abstract data type)Control (management)State (computer science)Computer scienceDimension (graph theory)Scale (ratio)MathematicsMathematical optimizationAlgorithmArtificial intelligenceCombinatoricsProgramming languageGeometryPhysicsQuantum mechanicsGene Regulatory Network AnalysisReceptor Mechanisms and SignalingComputational Drug Discovery Methods