Open AccessZeroing neural network model for solving a generalized linear time-varying matrix equation
Author(s) -
Huamin Zhang,
Huicheng Yin,
Bengbu Economics
Publication year - 2022
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
ISSN - 2473-6988
DOI - 10.3934/math.2022129
Subject(s) - transpose , artificial neural network , convergence (economics) , matrix (chemical analysis) , mathematics , computation , class (philosophy) , property (philosophy) , computer science , algorithm , artificial intelligence , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , economics , composite material , economic growth , philosophy , epistemology
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.