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

Huamin Zhang | Zendy; Hongcai Yin | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Huamin Zhang,

Hongcai Yin

Publication year - 2021

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research