Simply and effectively proved square characteristics of discrete-time zd solving systems of time-varying nonlinear equations

2015 IEEE International Conference on Information and Automation Pub Date : 2015-10-01 DOI:10.1109/ICINFA.2015.7279516

Yunong Zhang, H. Qiu, Chen Peng, Yanyan Shi, Hongzhou Tan

引用次数: 10

Abstract

A special class of continuous-time neural dynamics termed Zhang dynamics (ZD) has been investigated and generalized for solving the systems of time-varying nonlinear equations (STVNE). For possible digital hardware realization, the discrete-time ZD (DTZD) models are presented and investigated in this paper for solving the STVNE in the form of f(x(t), t) = 0 ∈ ℝn. For comparative purposes, the Newton iteration is also presented to solve the STVNE. Theoretical analysis, as simply and effectively proved, shows that the steady-state residual errors of the presented DTZD models are of O(τ2), which is further verified by the follow-up numerical experiments.

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简单有效地证明了时变非线性方程离散zd解系统的平方特性

研究了一类特殊的连续时间神经动力学——张氏动力学，并将其推广到求解时变非线性方程组。为了可能的数字硬件实现，本文提出并研究了离散时间ZD (DTZD)模型，用于求解f(x(t)， t) = 0∈∈∈n形式的STVNE。为了便于比较，本文还提出了求解STVNE的牛顿迭代方法。理论分析简单有效地证明了所提出的DTZD模型的稳态残差为0 (τ2)，后续的数值实验进一步验证了这一点。

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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2015 IEEE International Conference on Information and Automation

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