Time-Varying Polar Decomposition by Continuous-Time Model and Discrete-Time Algorithm of Zeroing Neural Network Using Zhang Time Discretization (ZTD)

Zanyu Tang, Liangjie Ming, Yunong Zhang, Runhao Shi
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Abstract

Time-varying polar decomposition becomes important and has many applications in potential fields. This paper first proposes a continuous-time model and a discrete-time algorithm of zeroing neural network (ZNN) for time-varying polar decomposition. The continuous-time polar decomposition (CTPD) model is obtained by using ZNN method. Besides, the discrete-time polar decomposition (DTPD) algorithm of ZNN is obtained by utilizing a 7-instant Zhang time discretization (ZTD) formula for being realized in digital computer readily. Finally, this paper investigates two numerical examples with different dimensions. The corresponding numerical results substantiate the relative effectiveness of the proposed continuous-time model and discrete-time algorithm of ZNN for time-varying polar decomposition.
连续时间模型时变极分解和张时间离散化(ZTD)归零神经网络的离散时间算法
时变极性分解在势场中具有重要的应用价值。本文首先提出了用于时变极分解的归零神经网络的连续时间模型和离散时间算法。采用ZNN方法建立了连续时间极坐标分解模型。此外,利用7瞬时张时间离散化(ZTD)公式,得到了ZNN的离散时间极分解(DTPD)算法,该算法易于在数字计算机上实现。最后,研究了两个不同维数的数值算例。相应的数值结果证实了ZNN连续时间模型和离散时间算法在时变极化分解中的相对有效性。
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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