

FOLLOWUS
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
School of Automation, Central South University, Changsha 410083, China
[ "ZHONG Jie,E-mail:zhongjie0615@gmail.com" ]
[ "LI Bo-wen,E-mail:qfhxjy@126.com" ]
[ "LIU Yang,E-mail:liuyang@zjnu.edu.cn" ]
[ "GUI Wei-hua,E-mail:gwh@csu.edu.cn" ]
Received:07 May 2019,
Revised:2019-;9-12,
Published Online:14 October 2019,
Published:2020-02
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Jie ZHONG, Bo-wen LI, Yang LIU, et al. Output feedback stabilizer design of Boolean networks based on network structure[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 247-259.
Jie ZHONG, Bo-wen LI, Yang LIU, et al. Output feedback stabilizer design of Boolean networks based on network structure[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(2): 247-259. DOI: 10.1631/FITEE.1900229.
在基因调控网络中,稳态结构可以用来表示细胞死亡或基因不受调控生长的进化行为。本文利用矩阵半张量积工具,分析与研究布尔网络输出反馈镇定器的设计。基于描述节点间耦合关系的网络结构信息,设计了输出反馈镇定器以实现全局稳定。与传统牵制控制器设计相比,输出反馈镇定器设计不再基于布尔网络的状态转移矩阵,可以有效确定牵制节点,降低计算复杂度。本文所提方法有效避免了计算2
n
×2
n
维的状态转移矩阵,这里
n
是布尔网络的节点数。最后,分别在一个信号转导网络和一个黑腹果蝇极性基因网络进行仿真模拟,证明该方法有效。结果表明,与传统布尔网络牵制控制相比,该方法更为简单、简洁。
In genetic regulatory networks
a stable configuration can represent the evolutionary behavior of cell death or unregulated growth in genes. We present analytical investigations on output feedback stabilizer design of Boolean networks (BNs) to achieve global stabilization via the semi-tensor product method. Based on network structure information describing coupling connections among nodes
an output feedback stabilizer is designed to achieve global stabilization. Compared 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 complexity. Our 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 BN. Finally
a signal transduction network and a D. melanogaster segmentation polarity gene network are presented to show the efficiency of the proposed method. Results are shown to be simple and concise
compared with traditional pinning control for BNs.
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