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General and Improved Five-Step Discrete-Time Zeroing Neural Dynamics Solving Linear Time-Varying Matrix Equation with Unknown Transpose

机译:一般和改进的五步离散时间归零神经动力学求解线性时变矩阵方程,不知名

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摘要

In this paper, a general five-step discrete-time zeroing neural dynamics (DTZND) model is proposed to solve linear time-varying matrix equation with unknown transpose. Specifically, the explicit continuous-time zeroing neural dynamics (CTZND) model is derived from the time-varying matrix equation with unknown transpose via Kronecker product and vectorization technique. Furthermore, a general five-step discretization formula is designed to approximate the first-order derivative of the target point, and the convergence condition is given. Thus, the general five-step DTZND model is obtained by using the general five-step discretization formula to discretize the CTZND model. Theoretical analyses present the stability and convergence of the proposed general five-step DTZND model. Numerical experiment results substantiate that the proposed DTZND model for solving linear time-varying matrix equation is stable and convergent with the theoretically analyzed errors. In addition, the improved DTZND models are provided in terms of accuracy and computational complexity, and verified by numerical experiments.
机译:在本文中,提出了一种普通的五步离散时间归零神经动力学(DTZND)模型,以解决具有未知转置的线性时变矩阵方程。具体地,显式连续时间归零神经动力学(CTZND)模型来自通过Kronecker产品和矢量化技术的未知转置的时变矩阵方程。此外,一般的五步离散化公式被设计为近似目标点的一阶导数,并给出收敛条件。因此,通过使用一般的五步离散化公式来实现一般的五步DTZND模型以使CTZND模型分开。理论分析呈现了所提出的五步DTZND模型的稳定性和收敛性。数值实验结果证实了用于求解线性时变矩阵方程的所提出的DTZND模型是稳定的,并在理论上分析的误差的收敛。此外,通过准确度和计算复杂性提供改进的DTZND模型,并通过数值实验验证。

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