New Models for Solving Time-Varying LU Decomposition by Using ZNN Method and ZeaD Formulas

Liangjie Ming, Yunong Zhang, Jinjin Guo, Xiao Liu, Zhonghua Li and Nan-Jing Huang

Journal of Mathematics, 2021, vol. 2021, 1-13

Abstract: In this paper, by employing the Zhang neural network (ZNN) method, an effective continuous-time LU decomposition (CTLUD) model is firstly proposed, analyzed, and investigated for solving the time-varying LU decomposition problem. Then, for the convenience of digital hardware realization, this paper proposes three discrete-time models by using Euler, 4-instant Zhang et al. discretization (ZeaD), and 8-instant ZeaD formulas to discretize the proposed CTLUD model, respectively. Furthermore, the proposed models are used to perform the LU decomposition of three time-varying matrices with different dimensions. Results indicate that the proposed models are effective for solving the time-varying LU decomposition problem, and the 8-instant ZeaD LU decomposition model has the highest precision among the three discrete-time models.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:6627298

DOI: 10.1155/2021/6627298

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