Gradient neural network model for the system of two linear matrix equations and applications

Jelena Dakić and Marko D. Petković

Applied Mathematics and Computation, 2024, vol. 481, issue C

Abstract: In this paper, the new type of Gradient Neural Network (GNN) model is proposed for the following linear system of matrix equations: AX=C,XB=D. The convergence analysis of given models is shown. The model is applied for the computation of the regular matrix inverse, as well as Moore-Penrose and Drazin generalized inverses. Some illustrative examples and simulations are given to verify theoretical results.

Keywords: Matrix equations; Gradient neural network; Moore-Penrose inverse; Drazin inverse (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300324003916
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:481:y:2024:i:c:s0096300324003916

DOI: 10.1016/j.amc.2024.128930

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().