Dimensionality Reduction Method for the Output Regulation of Boolean Control Networks

被引：2

作者：

Fu, Shihua ^{[1
]}

Feng, Jun-e ^{[2
]}

Zhao, Yuan ^{[3
]}

Wang, Jianjun ^{[4
]}

Pan, Jinfeng ^{[5
]}

机构：

[1] Liaocheng Univ, Res Ctr Semitensor Prod Matrices Theory & Applicat, Liaocheng 252000, Shandong, Peoples R China

[2] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China

[3] Qingdao Univ, Sch Automat, Qingdao 266071, Shandong, Peoples R China

[4] Univ Camerino, Sch Sci & Technol, I-62032 Camerino, Italy

[5] Weifang Univ, Sch Math & Informat Sci, Weifang 261061, Shandong, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2024年

基金：

中国国家自然科学基金;

关键词：

Regulation; Sufficient conditions; State feedback; Dimensionality reduction; Mathematical models; Computational complexity; Learning systems; Boolean control networks (BCNs); dimensionality reduction method; output regulation; semitensor product of matrices; state feedback controls; STABILIZATION CONTROL DESIGN; TRACKING; STABILITY; MODELS;

D O I：

10.1109/TNNLS.2024.3380247

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1 / 14

页数：14

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