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Cascading decomposition of Boolean control networks:a graph-theoretical method

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Frontiers of Information Technology & Electronic Engineering 2020年第2期21卷 304-315页

作者： Yi-feng LI Jian-dong ZHU Institute of Mathematics School of Mathematical SciencesNanjing Normal UniversityNanjing 210023China

Two types of cascading decomposition problems of Boolean control networks are investigated using a graph-theoretical method.A new graphic concept called nested perfect equal vertex partition(NPEVP)is *** on NPEVP,the necessary and sufficient graphic conditions for solvability of the cascading decomposition problems are *** the proposed graphic conditions,the logical coordinate transformations are constructively obtained to realize the corresponding cascading decomposition ***,two illustrative examples are provided to validate the results.

关键词： Boolean control networks semi-tensor product Cascading decomposition Graphic condition

Output tracking of delayed logical control networks with multi-constraint

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Frontiers of Information Technology & Electronic Engineering 2020年第2期21卷 316-323页

作者： Ya-ting ZHENG Jun-e FENG School of Mathematicst Shandong University Jinan 250100China

In this study,the output tracking of delayed logical control networks(DLCNs)with state and control constraints is further *** with other delays,state-dependent delay updates its value depending on the current state values and a pseudo-logical *** constraints mean that state values are constrained in a nonempty set and the design of the controller is *** the semi-tensor product of matrices,dynamical equations of DLCNs are converted into an algebraic description,and an equivalent augmented system is *** on the augmented system,the output tracking problem is transformed into a set stabilization problem.A deformation of the state transition matrix is computed,and a necessary and sufficient condition is derived for the output tracking of a DLCN with *** condition is easily verified by mathematical *** addition,the admissible state-feedback controller is designed to enable the outputs of the DLCN to track the reference ***,theoretical results are illustrated by an example.

关键词： Logical control networks Multi-constraint Output tracking Stabilization State-dependent delay semi-tensor product

New developments in control design techniques of logical control networks

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Frontiers of Information Technology & Electronic Engineering 2020年第2期21卷 220-233页

作者： Xiang-shan KONG Shu-ling WANG Hai-tao LI Fuad E.ALSAADI School of Mathematics and Statistics Shandong Normal UniversityJirian 250014China Department of Electrical and Computer Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia

The control design problem plays a fundamental role in the study of logical control networks(LCNs).This paper presents a detailed survey on new developments in control design techniques of ***,some preliminary results on the semi-tensor product method and LCNs are ***,we move on to some new developments for control design techniques of LCNs,including the reachable set approach,the pinning control technique,the control Lyapunov function approach,the event-triggered control technique,and the sampled-data control ***,an illustrative example is given to demonstrate the effectiveness of these techniques.

关键词： Logical control network Control design semi-tensor product of matrices

Output feedback stabilizer design of Boolean networks based on network structure

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Frontiers of Information Technology & Electronic Engineering 2020年第2期21卷 247-259页

作者： Jie ZHONG Bo-wen LI Yang LIU Wei-hua GUI College of Mathematics and Cmnputer Sciencef Zhejtang Normal University Jinhua 321004China School of Information Science and Engineering Southeast UniversityNanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China School of Automation Central South UniversityChangsha 410083China

In genetic regulatory networks,a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in *** present analytical investigations on output feedback stabilizer design of Boolean networks(BNs)to achieve global stabilization via the semi-tensor product *** on network structure information describing coupling connections among nodes,an output feedback stabilizer is designed to achieve global *** with the traditional pinning control design,the output feedback stabilizer design is not based on the state transition matrix of BNs,which can efficiently determine pinning control nodes and reduce computational *** proposed method is efficient in that the calculation of the state transition matrix with dimension 2^n×2^n is avoided;here n is the number of nodes in a ***,a signal transduction network and a *** segmentation polarity gene network are presented to show the efficiency of the proposed *** are shown to be simple and concise,compared with traditional pinning control for BNs.

关键词： Boolean networks Output feedback stabilizer Network structure semi-tensor product of matrices

Context-Aware System Modeling Based on Boolean Control Network

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Open Journal of Applied Sciences 2015年第11期5卷 661-668页

作者： M. Humayun Kabir M. Robiul Hoque Department of Applied Physics Electronics and Communication Engineering Islamic University Kushtia Bangladesh Department of Computer Science and Engineering Islamic University Kushtia Bangladesh

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

关键词： Mathematical Modeling Context-Aware System Smart Home Boolean Control Network semi-tensor Matrix Product