Zeroing neural network model for solving a generalized linear time-varying matrix equation

被引：3

作者：

Zhang, Huamin ^{[1
]}

Yin, Hongcai ^{[2
]}

机构：

[1] Anhui Sci & Technol Univ, Coll Informat & Network Engn, Bengbu 233030, Peoples R China

[2] Anhui Univ Finance & Econ, Sch Management Sci & Engn, Bengbu 233000, Peoples R China

来源：

AIMS MATHEMATICS | 2022年 / 7卷 / 02期

关键词：

linear time-varying matrix equation; zeroing neural network; convergence analysis; SYLVESTER EQUATION; PARAMETER-ESTIMATION; DYNAMICS; CONVERGENCE; DESIGN;

D O I：

10.3934/math.2022129

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The time-varying solution of a class generalized linear matrix equation with the transpose of an unknown matrix is discussed. The computation model is constructed and asymptotic convergence proof is given by using the zeroing neural network method. Using an activation function, the predefined-time convergence property and noise suppression strategy are discussed. Numerical examples are offered to illustrate the efficacy of the suggested zeroing neural network models.

引用

页码：2266 / 2280

页数：15

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