两个线性矩阵方程组的梯度神经网络模型及其应用

IF 3.5 2区数学 Q1 MATHEMATICS, APPLIED

Applied Mathematics and Computation Pub Date : 2024-07-14 DOI:10.1016/j.amc.2024.128930

Jelena Dakić , Marko D. Petković

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引用次数: 0

摘要

本文针对以下线性矩阵方程组提出了新型梯度神经网络（GNN）模型：AX=C，XB=D。文中展示了所给模型的收敛性分析。该模型适用于计算常规矩阵逆，以及 Moore-Penrose 和 Drazin 广义逆。给出了一些示例和模拟来验证理论结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

In this paper, the new type of Gradient Neural Network (GNN) model is proposed for the following linear system of matrix equations: $A X = C, X B = D$ . 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.

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来源期刊

Applied Mathematics and Computation 数学-应用数学

CiteScore

7.90

自引率

10.00%

发文量

755

审稿时长

36 days

期刊介绍： Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.